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-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
     <title>Overview: module code &#8212; modulation 0.0.1 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/alabaster.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/graphviz.css" />
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-    <link rel="index" title="Index" href="../genindex.html" />
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-            
-  <h1>All modules for which code is available</h1>
-<ul><li><a href="modulation.html">modulation</a></li>
-<ul><li><a href="modulation/classification/architecture/m5.html">modulation.classification.architecture.m5</a></li>
-<li><a href="modulation/classification/metrics/lwlrap.html">modulation.classification.metrics.lwlrap</a></li>
-<li><a href="modulation/classification/procedure.html">modulation.classification.procedure</a></li>
-<li><a href="modulation/classification/seperation/audio_unet.html">modulation.classification.seperation.audio_unet</a></li>
-<li><a href="modulation/data/audio/speech/seperation/dsd100.html">modulation.data.audio.speech.seperation.dsd100</a></li>
-<li><a href="modulation/data/signal_generator.html">modulation.data.signal_generator</a></li>
-<li><a href="modulation/entry_points/cli.html">modulation.entry_points.cli</a></li>
-<li><a href="modulation/entry_points/tensorboard_entry_point.html">modulation.entry_points.tensorboard_entry_point</a></li>
-<li><a href="modulation/regression/linear_predictive_coding.html">modulation.regression.linear_predictive_coding</a></li>
-<li><a href="modulation/regression/spectral_denoise.html">modulation.regression.spectral_denoise</a></li>
-<li><a href="modulation/signal_utilities/audio_utilities/filtering.html">modulation.signal_utilities.audio_utilities.filtering</a></li>
-<li><a href="modulation/signal_utilities/audio_utilities/persistence.html">modulation.signal_utilities.audio_utilities.persistence</a></li>
-<li><a href="modulation/signal_utilities/chopping.html">modulation.signal_utilities.chopping</a></li>
-<li><a href="modulation/signal_utilities/noise_generation/additive_noise.html">modulation.signal_utilities.noise_generation.additive_noise</a></li>
-<li><a href="modulation/signal_utilities/noise_generation/babble_noise.html">modulation.signal_utilities.noise_generation.babble_noise</a></li>
-<li><a href="modulation/signal_utilities/noise_generation/gaussian_noise.html">modulation.signal_utilities.noise_generation.gaussian_noise</a></li>
-<li><a href="modulation/signal_utilities/noise_generation/shaped_noise.html">modulation.signal_utilities.noise_generation.shaped_noise</a></li>
-<li><a href="modulation/signal_utilities/segmentation.html">modulation.signal_utilities.segmentation</a></li>
-<li><a href="modulation/signal_utilities/splitting.html">modulation.signal_utilities.splitting</a></li>
-<li><a href="modulation/torch_utilities/collation.html">modulation.torch_utilities.collation</a></li>
-<li><a href="modulation/torch_utilities/padding.html">modulation.torch_utilities.padding</a></li>
-<li><a href="modulation/torch_utilities/torch_transforms.html">modulation.torch_utilities.torch_transforms</a></li>
-<li><a href="modulation/visualisation/colorise_waveform.html">modulation.visualisation.colorise_waveform</a></li>
-<li><a href="modulation/visualisation/salience/colored.html">modulation.visualisation.salience.colored</a></li>
-</ul></ul>
+    <link href='../_static/pygments.css' rel='stylesheet' type='text/css'/>
+    <link href='../_static/alabaster.css' rel='stylesheet' type='text/css'/>
+    <link href='../_static/graphviz.css' rel='stylesheet' type='text/css'/>
+    <script data-url_root='../' id='documentation_options' src='../_static/documentation_options.js'></script>
+    <script src='../_static/jquery.js'></script>
+    <script src='../_static/underscore.js'></script>
+    <script src='../_static/_sphinx_javascript_frameworks_compat.js'></script>
+    <script src='../_static/doctools.js'></script>
+    <link href='Aivclab.github.io/modulation/_modules/index.html' rel='canonical'/>
+    <link href='../genindex.html' rel='index' title='Index'/>
+    <link href='../search.html' rel='search' title='Search'/>
+
+    <link href='../_static/custom.css' rel='stylesheet' type='text/css'/>
+
+
+    <meta content='width=device-width, initial-scale=0.9, maximum-scale=0.9' name='viewport'/>
+
+  </head>
+  <body>
+
+
+    <div class='document'>
+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
+
+
+          <div class='body' role='main'>
+
+            <h1>All modules for which code is available</h1>
+            <ul>
+              <li><a href='modulation.html'>modulation</a></li>
+              <ul>
+                <li><a href='modulation/classification/architecture/m5.html'>modulation.classification.architecture.m5</a></li>
+                <li><a href='modulation/classification/metrics/lwlrap.html'>modulation.classification.metrics.lwlrap</a></li>
+                <li><a href='modulation/classification/procedure.html'>modulation.classification.procedure</a></li>
+                <li><a href='modulation/classification/seperation/audio_unet.html'>modulation.classification.seperation.audio_unet</a></li>
+                <li><a href='modulation/data/audio/speech/seperation/dsd100.html'>modulation.data.audio.speech.seperation.dsd100</a></li>
+                <li><a href='modulation/data/signal_generator.html'>modulation.data.signal_generator</a></li>
+                <li><a href='modulation/entry_points/cli.html'>modulation.entry_points.cli</a></li>
+                <li><a href='modulation/entry_points/tensorboard_entry_point.html'>modulation.entry_points.tensorboard_entry_point</a></li>
+                <li><a href='modulation/regression/linear_predictive_coding.html'>modulation.regression.linear_predictive_coding</a></li>
+                <li><a href='modulation/regression/spectral_denoise.html'>modulation.regression.spectral_denoise</a></li>
+                <li><a href='modulation/signal_utilities/audio_utilities/filtering.html'>modulation.signal_utilities.audio_utilities.filtering</a></li>
+                <li><a href='modulation/signal_utilities/audio_utilities/persistence.html'>modulation.signal_utilities.audio_utilities.persistence</a></li>
+                <li><a href='modulation/signal_utilities/chopping.html'>modulation.signal_utilities.chopping</a></li>
+                <li><a href='modulation/signal_utilities/noise_generation/additive_noise.html'>modulation.signal_utilities.noise_generation.additive_noise</a></li>
+                <li><a href='modulation/signal_utilities/noise_generation/babble_noise.html'>modulation.signal_utilities.noise_generation.babble_noise</a></li>
+                <li><a href='modulation/signal_utilities/noise_generation/gaussian_noise.html'>modulation.signal_utilities.noise_generation.gaussian_noise</a></li>
+                <li><a href='modulation/signal_utilities/noise_generation/shaped_noise.html'>modulation.signal_utilities.noise_generation.shaped_noise</a></li>
+                <li><a href='modulation/signal_utilities/segmentation.html'>modulation.signal_utilities.segmentation</a></li>
+                <li><a href='modulation/signal_utilities/splitting.html'>modulation.signal_utilities.splitting</a></li>
+                <li><a href='modulation/torch_utilities/collation.html'>modulation.torch_utilities.collation</a></li>
+                <li><a href='modulation/torch_utilities/padding.html'>modulation.torch_utilities.padding</a></li>
+                <li><a href='modulation/torch_utilities/torch_transforms.html'>modulation.torch_utilities.torch_transforms</a></li>
+                <li><a href='modulation/visualisation/colorise_waveform.html'>modulation.visualisation.colorise_waveform</a></li>
+                <li><a href='modulation/visualisation/salience/colored.html'>modulation.visualisation.salience.colored</a></li>
+              </ul>
+            </ul>
 
           </div>
-          
+
         </div>
       </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-            <p class="logo"><a href="../index.html">
-              <img class="logo" src="../_static/mod.svg" alt="Logo"/>
-            </a></p>
-<h1 class="logo"><a href="../index.html">modulation</a></h1>
-
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-
-<h3>Navigation</h3>
-<ul>
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-</ul>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../getting_started.html">Getting Started</a></li>
-</ul>
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-<ul>
-  <li><a href="../index.html">Documentation overview</a><ul>
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-  <h3 id="searchlabel">Quick search</h3>
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-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
-      <input type="submit" value="Go" />
-    </form>
-    </div>
-</div>
-<script>document.getElementById('searchbox').style.display = "block"</script>
-
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+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <p class='logo'><a href='../index.html'>
+            <img alt='Logo' class='logo' src='../_static/mod.svg'/>
+          </a></p>
+          <h1 class='logo'><a href='../index.html'>modulation</a></h1>
+
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+          <h3>Navigation</h3>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../generated/modulation.html'>modulation</a></li>
+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../index.html'>Documentation overview</a>
+                <ul>
+                </ul>
+              </li>
+            </ul>
+          </div>
+          <div id='searchbox' role='search' style='display: none'>
+            <h3 id='searchlabel'>Quick search</h3>
+            <div class='searchformwrapper'>
+              <form action='../search.html' class='search' method='get'>
+                <input aria-labelledby='searchlabel' autocapitalize='off' autocomplete='off' autocorrect='off' name='q' spellcheck='false' type='text'/>
+                <input type='submit' value='Go'/>
+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
 
 
         </div>
       </div>
-      <div class="clearer"></div>
+      <div class='clearer'></div>
     </div>
-    <div class="footer">
+    <div class='footer'>
       &copy;.
-      
+
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
-      
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
+
     </div>
 
-    
 
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/docs/github/html/_modules/modulation.html b/docs/github/html/_modules/modulation.html
index 7e5a49a..1f7c66e 100644
--- a/docs/github/html/_modules/modulation.html
+++ b/docs/github/html/_modules/modulation.html
@@ -1,225 +1,217 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
     <title>modulation &#8212; modulation 0.0.1 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/alabaster.css" />
-    <link rel="stylesheet" type="text/css" href="../_static/graphviz.css" />
-    <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-    <script src="../_static/jquery.js"></script>
-    <script src="../_static/underscore.js"></script>
-    <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
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-    <link rel="canonical" href="Aivclab.github.io/modulation/_modules/modulation.html" />
-    <link rel="index" title="Index" href="../genindex.html" />
-    <link rel="search" title="Search" href="../search.html" />
-   
-  <link rel="stylesheet" href="../_static/custom.css" type="text/css" />
-  
-  
-  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
-
-  </head><body>
-  
-
-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          
-
-          <div class="body" role="main">
-            
-  <h1>Source code for modulation</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="kn">import</span> <span class="nn">datetime</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
-
-<span class="kn">import</span> <span class="nn">pkg_resources</span>
-<span class="kn">from</span> <span class="nn">apppath</span> <span class="kn">import</span> <span class="n">AppPath</span>
-
-<span class="n">__project__</span> <span class="o">=</span> <span class="s2">&quot;Modulation&quot;</span>
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;0.0.1&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-<span class="s2">.. module:: modulation</span>
-<span class="s2">   :platform: Unix, Windows</span>
-<span class="s2">   :synopsis: A set of tools build for the signal processing.</span>
-
-<span class="s2">.. moduleauthor:: Christian Heider Nielsen &lt;christian.heider@alexandra.dk&gt;</span>
-
-<span class="s2">Created on 27/04/2019</span>
-
-<span class="s2">@author: cnheider</span>
-<span class="s2">&quot;&quot;&quot;</span>
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;PROJECT_APP_PATH&quot;</span><span class="p">,</span> <span class="s2">&quot;PROJECT_NAME&quot;</span><span class="p">,</span> <span class="s2">&quot;PROJECT_VERSION&quot;</span><span class="p">,</span> <span class="s2">&quot;get_version&quot;</span><span class="p">,</span> <span class="s1">&#39;PROJECT_ORGANISATION&#39;</span><span class="p">,</span>
-           <span class="s1">&#39;PROJECT_AUTHOR&#39;</span><span class="p">,</span> <span class="s1">&#39;PROJECT_YEAR&#39;</span><span class="p">,</span> <span class="s1">&#39;INCLUDE_PROJECT_READMES&#39;</span><span class="p">,</span> <span class="s1">&#39;PACKAGE_DATA_PATH&#39;</span><span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
-<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
-
-
-<span class="k">def</span> <span class="nf">dist_is_editable</span><span class="p">(</span><span class="n">dist</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-  <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">  Return True if given Distribution is an editable installation.&quot;&quot;&quot;</span>
-  <span class="kn">import</span> <span class="nn">sys</span>
-
-  <span class="k">for</span> <span class="n">path_item</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="p">:</span>
-    <span class="n">egg_link</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">path_item</span><span class="p">)</span> <span class="o">/</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">dist</span><span class="o">.</span><span class="n">project_name</span><span class="si">}</span><span class="s2">.egg-link&quot;</span>
-    <span class="k">if</span> <span class="n">egg_link</span><span class="o">.</span><span class="n">is_file</span><span class="p">():</span>
-      <span class="k">return</span> <span class="kc">True</span>
-  <span class="k">return</span> <span class="kc">False</span>
-
-
-<span class="n">PROJECT_NAME</span> <span class="o">=</span> <span class="n">__project__</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="s2">&quot;_&quot;</span><span class="p">)</span>
-<span class="n">PROJECT_VERSION</span> <span class="o">=</span> <span class="n">__version__</span>
-<span class="n">PROJECT_YEAR</span> <span class="o">=</span> <span class="mi">2018</span>
-<span class="n">PROJECT_AUTHOR</span> <span class="o">=</span> <span class="n">__author__</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="s2">&quot;_&quot;</span><span class="p">)</span>
-<span class="n">PROJECT_ORGANISATION</span> <span class="o">=</span> <span class="s2">&quot;Aivclab&quot;</span>
-<span class="n">PROJECT_APP_PATH</span> <span class="o">=</span> <span class="n">AppPath</span><span class="p">(</span><span class="n">app_name</span> <span class="o">=</span> <span class="n">PROJECT_NAME</span><span class="p">,</span> <span class="n">app_author</span> <span class="o">=</span> <span class="n">PROJECT_AUTHOR</span><span class="p">)</span>
-<span class="n">PACKAGE_DATA_PATH</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">pkg_resources</span><span class="o">.</span><span class="n">resource_filename</span><span class="p">(</span><span class="n">PROJECT_NAME</span><span class="p">,</span> <span class="s2">&quot;data&quot;</span><span class="p">))</span>
-<span class="n">INCLUDE_PROJECT_READMES</span> <span class="o">=</span> <span class="kc">False</span>
-
-<span class="n">distributions</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="o">.</span><span class="n">key</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">pkg_resources</span><span class="o">.</span><span class="n">working_set</span><span class="p">}</span>
-<span class="k">if</span> <span class="n">PROJECT_NAME</span> <span class="ow">in</span> <span class="n">distributions</span><span class="p">:</span>
-  <span class="n">distribution</span> <span class="o">=</span> <span class="n">distributions</span><span class="p">[</span><span class="n">PROJECT_NAME</span><span class="p">]</span>
-  <span class="n">DEVELOP</span> <span class="o">=</span> <span class="n">dist_is_editable</span><span class="p">(</span><span class="n">distribution</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-  <span class="n">DEVELOP</span> <span class="o">=</span> <span class="kc">True</span>
-
-
-<div class="viewcode-block" id="get_version"><a class="viewcode-back" href="../generated/modulation.get_version.html#modulation.get_version">[docs]</a><span class="k">def</span> <span class="nf">get_version</span><span class="p">(</span><span class="n">append_time</span><span class="p">:</span> <span class="n">Any</span> <span class="o">=</span> <span class="n">DEVELOP</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-  <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">  :param append_time:</span>
-<span class="sd">  :type append_time:</span>
-<span class="sd">  :return:</span>
-<span class="sd">  :rtype:</span>
-<span class="sd">  &quot;&quot;&quot;</span>
-  <span class="n">version</span> <span class="o">=</span> <span class="n">__version__</span>
-  <span class="k">if</span> <span class="ow">not</span> <span class="n">version</span><span class="p">:</span>
-    <span class="n">version</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;VERSION&quot;</span><span class="p">,</span> <span class="s2">&quot;0.0.0&quot;</span><span class="p">)</span>
-
-  <span class="k">if</span> <span class="n">append_time</span><span class="p">:</span>
-    <span class="n">now</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">utcnow</span><span class="p">()</span>
-    <span class="n">date_version</span> <span class="o">=</span> <span class="n">now</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%Y%m</span><span class="si">%d</span><span class="s2">%H%M%S&quot;</span><span class="p">)</span>
-    <span class="c1"># date_version = time.time()</span>
-
-    <span class="k">if</span> <span class="n">version</span><span class="p">:</span>
-      <span class="c1"># Most git tags are prefixed with &#39;v&#39; (example: v1.2.3) this is</span>
-      <span class="c1"># never desirable for artifact repositories, so we strip the</span>
-      <span class="c1"># leading &#39;v&#39; if it&#39;s present.</span>
-      <span class="n">version</span> <span class="o">=</span> <span class="p">(</span>
-          <span class="n">version</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
-          <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">version</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">version</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;v&quot;</span><span class="p">)</span>
-          <span class="k">else</span> <span class="n">version</span>
-      <span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-      <span class="c1"># Default version is an ISO8601 compliant datetime. PyPI doesn&#39;t allow</span>
-      <span class="c1"># the colon &#39;:&#39; character in its versions, and time is required to allow</span>
-      <span class="c1"># for multiple publications to master in one day. This datetime string</span>
-      <span class="c1"># uses the &#39;basic&#39; ISO8601 format for both its date and time components</span>
-      <span class="c1"># to avoid issues with the colon character (ISO requires that date and</span>
-      <span class="c1"># time components of a date-time string must be uniformly basic or</span>
-      <span class="c1"># extended, which is why the date component does not have dashes.</span>
-      <span class="c1">#</span>
-      <span class="c1"># Publications using datetime versions should only be made from master</span>
-      <span class="c1"># to represent the HEAD moving forward.</span>
-      <span class="n">warn</span><span class="p">(</span>
-          <span class="sa">f</span><span class="s2">&quot;Environment variable VERSION is not set, only using datetime: </span><span class="si">{</span><span class="n">date_version</span><span class="si">}</span><span class="s2">&quot;</span>
-          <span class="p">)</span>
-
-      <span class="c1"># warn(f&#39;Environment variable VERSION is not set, only using timestamp: {version}&#39;)</span>
-
-    <span class="n">version</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">version</span><span class="si">}</span><span class="s2">.</span><span class="si">{</span><span class="n">date_version</span><span class="si">}</span><span class="s2">&quot;</span>
-
-  <span class="k">return</span> <span class="n">version</span></div>
-
-
-<span class="k">if</span> <span class="n">__version__</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-  <span class="n">__version__</span> <span class="o">=</span> <span class="n">get_version</span><span class="p">(</span><span class="n">append_time</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
-
-<span class="n">__version_info__</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">segment</span><span class="p">)</span> <span class="k">for</span> <span class="n">segment</span> <span class="ow">in</span> <span class="n">__version__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">))</span>
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-  <span class="nb">print</span><span class="p">(</span><span class="n">PROJECT_APP_PATH</span><span class="o">.</span><span class="n">user_log</span><span class="p">)</span>
-</pre></div>
+    <link href='../_static/pygments.css' rel='stylesheet' type='text/css'/>
+    <link href='../_static/alabaster.css' rel='stylesheet' type='text/css'/>
+    <link href='../_static/graphviz.css' rel='stylesheet' type='text/css'/>
+    <script data-url_root='../' id='documentation_options' src='../_static/documentation_options.js'></script>
+    <script src='../_static/jquery.js'></script>
+    <script src='../_static/underscore.js'></script>
+    <script src='../_static/_sphinx_javascript_frameworks_compat.js'></script>
+    <script src='../_static/doctools.js'></script>
+    <link href='Aivclab.github.io/modulation/_modules/modulation.html' rel='canonical'/>
+    <link href='../genindex.html' rel='index' title='Index'/>
+    <link href='../search.html' rel='search' title='Search'/>
+
+    <link href='../_static/custom.css' rel='stylesheet' type='text/css'/>
+
+
+    <meta content='width=device-width, initial-scale=0.9, maximum-scale=0.9' name='viewport'/>
+
+  </head>
+  <body>
+
+
+    <div class='document'>
+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
+
+
+          <div class='body' role='main'>
+
+            <h1>Source code for modulation</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='kn'>import</span> <span class='nn'>datetime</span>
+<span class='kn'>import</span> <span class='nn'>os</span>
+<span class='kn'>from</span> <span class='nn'>warnings</span> <span class='kn'>import</span> <span class='n'>warn</span>
+
+<span class='kn'>import</span> <span class='nn'>pkg_resources</span>
+<span class='kn'>from</span> <span class='nn'>apppath</span> <span class='kn'>import</span> <span class='n'>AppPath</span>
+
+<span class='n'>__project__</span> <span class='o'>=</span> <span class='s2'>&quot;Modulation&quot;</span>
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='n'>__version__</span> <span class='o'>=</span> <span class='s2'>&quot;0.0.1&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+<span class='s2'>.. module:: modulation</span>
+<span class='s2'>   :platform: Unix, Windows</span>
+<span class='s2'>   :synopsis: A set of tools build for the signal processing.</span>
+
+<span class='s2'>.. moduleauthor:: Christian Heider Nielsen &lt;christian.heider@alexandra.dk&gt;</span>
+
+<span class='s2'>Created on 27/04/2019</span>
+
+<span class='s2'>@author: cnheider</span>
+<span class='s2'>&quot;&quot;&quot;</span>
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[</span><span class='s2'>&quot;PROJECT_APP_PATH&quot;</span><span class='p'>,</span> <span class='s2'>&quot;PROJECT_NAME&quot;</span><span class='p'>,</span> <span class='s2'>&quot;PROJECT_VERSION&quot;</span><span class='p'>,</span> <span class='s2'>&quot;get_version&quot;</span><span class='p'>,</span> <span class='s1'>&#39;PROJECT_ORGANISATION&#39;</span><span class='p'>,</span>
+           <span class='s1'>&#39;PROJECT_AUTHOR&#39;</span><span class='p'>,</span> <span class='s1'>&#39;PROJECT_YEAR&#39;</span><span class='p'>,</span> <span class='s1'>&#39;INCLUDE_PROJECT_READMES&#39;</span><span class='p'>,</span> <span class='s1'>&#39;PACKAGE_DATA_PATH&#39;</span><span class='p'>]</span>
+
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Any</span>
+<span class='kn'>from</span> <span class='nn'>pathlib</span> <span class='kn'>import</span> <span class='n'>Path</span>
+
+
+<span class='k'>def</span> <span class='nf'>dist_is_editable</span><span class='p'>(</span><span class='n'>dist</span><span class='p'>:</span> <span class='n'>Any</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='nb'>bool</span><span class='p'>:</span>
+  <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>  Return True if given Distribution is an editable installation.&quot;&quot;&quot;</span>
+  <span class='kn'>import</span> <span class='nn'>sys</span>
+
+  <span class='k'>for</span> <span class='n'>path_item</span> <span class='ow'>in</span> <span class='n'>sys</span><span class='o'>.</span><span class='n'>path</span><span class='p'>:</span>
+    <span class='n'>egg_link</span> <span class='o'>=</span> <span class='n'>Path</span><span class='p'>(</span><span class='n'>path_item</span><span class='p'>)</span> <span class='o'>/</span> <span class='sa'>f</span><span class='s2'>&quot;</span><span class='si'>{</span><span class='n'>dist</span><span class='o'>.</span><span class='n'>project_name</span><span class='si'>}</span><span class='s2'>.egg-link&quot;</span>
+    <span class='k'>if</span> <span class='n'>egg_link</span><span class='o'>.</span><span class='n'>is_file</span><span class='p'>():</span>
+      <span class='k'>return</span> <span class='kc'>True</span>
+  <span class='k'>return</span> <span class='kc'>False</span>
+
+
+<span class='n'>PROJECT_NAME</span> <span class='o'>=</span> <span class='n'>__project__</span><span class='o'>.</span><span class='n'>lower</span><span class='p'>()</span><span class='o'>.</span><span class='n'>strip</span><span class='p'>()</span><span class='o'>.</span><span class='n'>replace</span><span class='p'>(</span><span class='s2'>&quot; &quot;</span><span class='p'>,</span> <span class='s2'>&quot;_&quot;</span><span class='p'>)</span>
+<span class='n'>PROJECT_VERSION</span> <span class='o'>=</span> <span class='n'>__version__</span>
+<span class='n'>PROJECT_YEAR</span> <span class='o'>=</span> <span class='mi'>2018</span>
+<span class='n'>PROJECT_AUTHOR</span> <span class='o'>=</span> <span class='n'>__author__</span><span class='o'>.</span><span class='n'>lower</span><span class='p'>()</span><span class='o'>.</span><span class='n'>strip</span><span class='p'>()</span><span class='o'>.</span><span class='n'>replace</span><span class='p'>(</span><span class='s2'>&quot; &quot;</span><span class='p'>,</span> <span class='s2'>&quot;_&quot;</span><span class='p'>)</span>
+<span class='n'>PROJECT_ORGANISATION</span> <span class='o'>=</span> <span class='s2'>&quot;Aivclab&quot;</span>
+<span class='n'>PROJECT_APP_PATH</span> <span class='o'>=</span> <span class='n'>AppPath</span><span class='p'>(</span><span class='n'>app_name</span> <span class='o'>=</span> <span class='n'>PROJECT_NAME</span><span class='p'>,</span> <span class='n'>app_author</span> <span class='o'>=</span> <span class='n'>PROJECT_AUTHOR</span><span class='p'>)</span>
+<span class='n'>PACKAGE_DATA_PATH</span> <span class='o'>=</span> <span class='n'>Path</span><span class='p'>(</span><span class='n'>pkg_resources</span><span class='o'>.</span><span class='n'>resource_filename</span><span class='p'>(</span><span class='n'>PROJECT_NAME</span><span class='p'>,</span> <span class='s2'>&quot;data&quot;</span><span class='p'>))</span>
+<span class='n'>INCLUDE_PROJECT_READMES</span> <span class='o'>=</span> <span class='kc'>False</span>
+
+<span class='n'>distributions</span> <span class='o'>=</span> <span class='p'>{</span><span class='n'>v</span><span class='o'>.</span><span class='n'>key</span><span class='p'>:</span> <span class='n'>v</span> <span class='k'>for</span> <span class='n'>v</span> <span class='ow'>in</span> <span class='n'>pkg_resources</span><span class='o'>.</span><span class='n'>working_set</span><span class='p'>}</span>
+<span class='k'>if</span> <span class='n'>PROJECT_NAME</span> <span class='ow'>in</span> <span class='n'>distributions</span><span class='p'>:</span>
+  <span class='n'>distribution</span> <span class='o'>=</span> <span class='n'>distributions</span><span class='p'>[</span><span class='n'>PROJECT_NAME</span><span class='p'>]</span>
+  <span class='n'>DEVELOP</span> <span class='o'>=</span> <span class='n'>dist_is_editable</span><span class='p'>(</span><span class='n'>distribution</span><span class='p'>)</span>
+<span class='k'>else</span><span class='p'>:</span>
+  <span class='n'>DEVELOP</span> <span class='o'>=</span> <span class='kc'>True</span>
+
+
+<div class='viewcode-block' id='get_version'><a class='viewcode-back' href='../generated/modulation.get_version.html#modulation.get_version'>[docs]</a><span class='k'>def</span> <span class='nf'>get_version</span><span class='p'>(</span><span class='n'>append_time</span><span class='p'>:</span> <span class='n'>Any</span> <span class='o'>=</span> <span class='n'>DEVELOP</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='nb'>str</span><span class='p'>:</span>
+  <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>  :param append_time:</span>
+<span class='sd'>  :type append_time:</span>
+<span class='sd'>  :return:</span>
+<span class='sd'>  :rtype:</span>
+<span class='sd'>  &quot;&quot;&quot;</span>
+  <span class='n'>version</span> <span class='o'>=</span> <span class='n'>__version__</span>
+  <span class='k'>if</span> <span class='ow'>not</span> <span class='n'>version</span><span class='p'>:</span>
+    <span class='n'>version</span> <span class='o'>=</span> <span class='n'>os</span><span class='o'>.</span><span class='n'>getenv</span><span class='p'>(</span><span class='s2'>&quot;VERSION&quot;</span><span class='p'>,</span> <span class='s2'>&quot;0.0.0&quot;</span><span class='p'>)</span>
+
+  <span class='k'>if</span> <span class='n'>append_time</span><span class='p'>:</span>
+    <span class='n'>now</span> <span class='o'>=</span> <span class='n'>datetime</span><span class='o'>.</span><span class='n'>datetime</span><span class='o'>.</span><span class='n'>utcnow</span><span class='p'>()</span>
+    <span class='n'>date_version</span> <span class='o'>=</span> <span class='n'>now</span><span class='o'>.</span><span class='n'>strftime</span><span class='p'>(</span><span class='s2'>&quot;%Y%m</span><span class='si'>%d</span><span class='s2'>%H%M%S&quot;</span><span class='p'>)</span>
+    <span class='c1'># date_version = time.time()</span>
+
+    <span class='k'>if</span> <span class='n'>version</span><span class='p'>:</span>
+      <span class='c1'># Most git tags are prefixed with &#39;v&#39; (example: v1.2.3) this is</span>
+      <span class='c1'># never desirable for artifact repositories, so we strip the</span>
+      <span class='c1'># leading &#39;v&#39; if it&#39;s present.</span>
+      <span class='n'>version</span> <span class='o'>=</span> <span class='p'>(</span>
+          <span class='n'>version</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:]</span>
+          <span class='k'>if</span> <span class='nb'>isinstance</span><span class='p'>(</span><span class='n'>version</span><span class='p'>,</span> <span class='nb'>str</span><span class='p'>)</span> <span class='ow'>and</span> <span class='n'>version</span><span class='o'>.</span><span class='n'>startswith</span><span class='p'>(</span><span class='s2'>&quot;v&quot;</span><span class='p'>)</span>
+          <span class='k'>else</span> <span class='n'>version</span>
+      <span class='p'>)</span>
+    <span class='k'>else</span><span class='p'>:</span>
+      <span class='c1'># Default version is an ISO8601 compliant datetime. PyPI doesn&#39;t allow</span>
+      <span class='c1'># the colon &#39;:&#39; character in its versions, and time is required to allow</span>
+      <span class='c1'># for multiple publications to master in one day. This datetime string</span>
+      <span class='c1'># uses the &#39;basic&#39; ISO8601 format for both its date and time components</span>
+      <span class='c1'># to avoid issues with the colon character (ISO requires that date and</span>
+      <span class='c1'># time components of a date-time string must be uniformly basic or</span>
+      <span class='c1'># extended, which is why the date component does not have dashes.</span>
+      <span class='c1'>#</span>
+      <span class='c1'># Publications using datetime versions should only be made from master</span>
+      <span class='c1'># to represent the HEAD moving forward.</span>
+      <span class='n'>warn</span><span class='p'>(</span>
+          <span class='sa'>f</span><span class='s2'>&quot;Environment variable VERSION is not set, only using datetime: </span><span class='si'>{</span><span class='n'>date_version</span><span class='si'>}</span><span class='s2'>&quot;</span>
+          <span class='p'>)</span>
+
+      <span class='c1'># warn(f&#39;Environment variable VERSION is not set, only using timestamp: {version}&#39;)</span>
+
+    <span class='n'>version</span> <span class='o'>=</span> <span class='sa'>f</span><span class='s2'>&quot;</span><span class='si'>{</span><span class='n'>version</span><span class='si'>}</span><span class='s2'>.</span><span class='si'>{</span><span class='n'>date_version</span><span class='si'>}</span><span class='s2'>&quot;</span>
+
+  <span class='k'>return</span> <span class='n'>version</span></div>
+
+
+<span class='k'>if</span> <span class='n'>__version__</span> <span class='ow'>is</span> <span class='kc'>None</span><span class='p'>:</span>
+  <span class='n'>__version__</span> <span class='o'>=</span> <span class='n'>get_version</span><span class='p'>(</span><span class='n'>append_time</span> <span class='o'>=</span> <span class='kc'>True</span><span class='p'>)</span>
+
+<span class='n'>__version_info__</span> <span class='o'>=</span> <span class='nb'>tuple</span><span class='p'>(</span><span class='nb'>int</span><span class='p'>(</span><span class='n'>segment</span><span class='p'>)</span> <span class='k'>for</span> <span class='n'>segment</span> <span class='ow'>in</span> <span class='n'>__version__</span><span class='o'>.</span><span class='n'>split</span><span class='p'>(</span><span class='s2'>&quot;.&quot;</span><span class='p'>))</span>
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+  <span class='nb'>print</span><span class='p'>(</span><span class='n'>PROJECT_APP_PATH</span><span class='o'>.</span><span class='n'>user_log</span><span class='p'>)</span>
+</pre>
+            </div>
 
           </div>
-          
+
         </div>
       </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-            <p class="logo"><a href="../index.html">
-              <img class="logo" src="../_static/mod.svg" alt="Logo"/>
-            </a></p>
-<h1 class="logo"><a href="../index.html">modulation</a></h1>
-
-
-
-
-
-
-
-
-<h3>Navigation</h3>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../generated/modulation.html">modulation</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notes</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../getting_started.html">Getting Started</a></li>
-</ul>
-
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../index.html">Documentation overview</a><ul>
-  <li><a href="index.html">Module code</a><ul>
-  </ul></li>
-  </ul></li>
-</ul>
-</div>
-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
-      <input type="submit" value="Go" />
-    </form>
-    </div>
-</div>
-<script>document.getElementById('searchbox').style.display = "block"</script>
-
-
-
-
-
-
+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <p class='logo'><a href='../index.html'>
+            <img alt='Logo' class='logo' src='../_static/mod.svg'/>
+          </a></p>
+          <h1 class='logo'><a href='../index.html'>modulation</a></h1>
+
+
+          <h3>Navigation</h3>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../generated/modulation.html'>modulation</a></li>
+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../index.html'>Documentation overview</a>
+                <ul>
+                  <li><a href='index.html'>Module code</a>
+                    <ul>
+                    </ul>
+                  </li>
+                </ul>
+              </li>
+            </ul>
+          </div>
+          <div id='searchbox' role='search' style='display: none'>
+            <h3 id='searchlabel'>Quick search</h3>
+            <div class='searchformwrapper'>
+              <form action='../search.html' class='search' method='get'>
+                <input aria-labelledby='searchlabel' autocapitalize='off' autocomplete='off' autocorrect='off' name='q' spellcheck='false' type='text'/>
+                <input type='submit' value='Go'/>
+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
 
 
         </div>
       </div>
-      <div class="clearer"></div>
+      <div class='clearer'></div>
     </div>
-    <div class="footer">
+    <div class='footer'>
       &copy;.
-      
+
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
-      
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
+
     </div>
 
-    
 
-    
   </body>
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\ No newline at end of file
diff --git a/docs/github/html/_modules/modulation/classification/architecture/m5.html b/docs/github/html/_modules/modulation/classification/architecture/m5.html
index 89cc18b..610341e 100644
--- a/docs/github/html/_modules/modulation/classification/architecture/m5.html
+++ b/docs/github/html/_modules/modulation/classification/architecture/m5.html
@@ -1,176 +1,170 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
     <title>modulation.classification.architecture.m5 &#8212; modulation 0.0.1 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../../_static/alabaster.css" />
-    <link rel="stylesheet" type="text/css" href="../../../../_static/graphviz.css" />
-    <script data-url_root="../../../../" id="documentation_options" src="../../../../_static/documentation_options.js"></script>
-    <script src="../../../../_static/jquery.js"></script>
-    <script src="../../../../_static/underscore.js"></script>
-    <script src="../../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
-    <script src="../../../../_static/doctools.js"></script>
-    <link rel="canonical" href="Aivclab.github.io/modulation/_modules/modulation/classification/architecture/m5.html" />
-    <link rel="index" title="Index" href="../../../../genindex.html" />
-    <link rel="search" title="Search" href="../../../../search.html" />
-   
-  <link rel="stylesheet" href="../../../../_static/custom.css" type="text/css" />
-  
-  
-  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
-
-  </head><body>
-  
-
-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          
-
-          <div class="body" role="main">
-            
-  <h1>Source code for modulation.classification.architecture.m5</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 18-11-2020</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;M5&quot;</span><span class="p">]</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-
-
-<div class="viewcode-block" id="M5"><a class="viewcode-back" href="../../../../generated/modulation.classification.architecture.m5.M5.html#modulation.classification.architecture.m5.M5">[docs]</a><span class="k">class</span> <span class="nc">M5</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    M5 network architecture</span>
-<span class="sd">    described in `this paper &lt;https://arxiv.org/pdf/1610.00087.pdf&gt;`__. An</span>
-<span class="sd">    important aspect of models processing raw audio data is the receptive</span>
-<span class="sd">    field of their first layer’s filters. The model’s first filter is length</span>
-<span class="sd">    80 so when processing audio sampled at 8kHz the receptive field is</span>
-<span class="sd">    around 10ms (and at 4kHz, around 20 ms). This size is similar to speech</span>
-<span class="sd">    processing applications that often use receptive fields ranging from</span>
-<span class="sd">    20ms to 40ms.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="M5.__init__"><a class="viewcode-back" href="../../../../generated/modulation.classification.architecture.m5.M5.html#modulation.classification.architecture.m5.M5.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_input</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_output</span><span class="o">=</span><span class="mi">35</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">n_channel</span><span class="o">=</span><span class="mi">32</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="n">n_input</span><span class="p">,</span> <span class="n">n_channel</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">80</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="n">n_channel</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">MaxPool1d</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="n">n_channel</span><span class="p">,</span> <span class="n">n_channel</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bn2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="n">n_channel</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pool2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">MaxPool1d</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="n">n_channel</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">n_channel</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bn3</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n_channel</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pool3</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">MaxPool1d</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv4</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n_channel</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">n_channel</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bn4</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n_channel</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pool4</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">MaxPool1d</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n_channel</span><span class="p">,</span> <span class="n">n_output</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="M5.forward"><a class="viewcode-back" href="../../../../generated/modulation.classification.architecture.m5.M5.html#modulation.classification.architecture.m5.M5.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">        :param x:</span>
-<span class="sd">        :type x:</span>
-<span class="sd">        :return:</span>
-<span class="sd">        :rtype:</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bn1</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bn2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pool1</span><span class="p">(</span><span class="n">x</span><span class="p">))))</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bn3</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv3</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pool2</span><span class="p">(</span><span class="n">x</span><span class="p">))))</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bn4</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv4</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pool3</span><span class="p">(</span><span class="n">x</span><span class="p">))))</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool4</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
-        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">avg_pool1d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span></div></div>
-</pre></div>
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+            <h1>Source code for modulation.classification.architecture.m5</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 18-11-2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[</span><span class='s2'>&quot;M5&quot;</span><span class='p'>]</span>
 
+<span class='kn'>import</span> <span class='nn'>torch</span>
 
 
+<div class='viewcode-block' id='M5'><a class='viewcode-back' href='../../../../generated/modulation.classification.architecture.m5.M5.html#modulation.classification.architecture.m5.M5'>[docs]</a><span class='k'>class</span> <span class='nc'>M5</span><span class='p'>(</span><span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Module</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    M5 network architecture</span>
+<span class='sd'>    described in `this paper &lt;https://arxiv.org/pdf/1610.00087.pdf&gt;`__. An</span>
+<span class='sd'>    important aspect of models processing raw audio data is the receptive</span>
+<span class='sd'>    field of their first layer’s filters. The model’s first filter is length</span>
+<span class='sd'>    80 so when processing audio sampled at 8kHz the receptive field is</span>
+<span class='sd'>    around 10ms (and at 4kHz, around 20 ms). This size is similar to speech</span>
+<span class='sd'>    processing applications that often use receptive fields ranging from</span>
+<span class='sd'>    20ms to 40ms.</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
 
+<div class='viewcode-block' id='M5.__init__'><a class='viewcode-back' href='../../../../generated/modulation.classification.architecture.m5.M5.html#modulation.classification.architecture.m5.M5.__init__'>[docs]</a>    <span class='k'>def</span> <span class='fm'>__init__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>n_input</span><span class='o'>=</span><span class='mi'>1</span><span class='p'>,</span> <span class='n'>n_output</span><span class='o'>=</span><span class='mi'>35</span><span class='p'>,</span> <span class='n'>stride</span><span class='o'>=</span><span class='mi'>16</span><span class='p'>,</span> <span class='n'>n_channel</span><span class='o'>=</span><span class='mi'>32</span><span class='p'>):</span>
+        <span class='nb'>super</span><span class='p'>()</span><span class='o'>.</span><span class='fm'>__init__</span><span class='p'>()</span>
 
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>conv1</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Conv1d</span><span class='p'>(</span><span class='n'>n_input</span><span class='p'>,</span> <span class='n'>n_channel</span><span class='p'>,</span> <span class='n'>kernel_size</span><span class='o'>=</span><span class='mi'>80</span><span class='p'>,</span> <span class='n'>stride</span><span class='o'>=</span><span class='n'>stride</span><span class='p'>)</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>bn1</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>BatchNorm1d</span><span class='p'>(</span><span class='n'>n_channel</span><span class='p'>)</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>pool1</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>MaxPool1d</span><span class='p'>(</span><span class='mi'>4</span><span class='p'>)</span>
 
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>conv2</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Conv1d</span><span class='p'>(</span><span class='n'>n_channel</span><span class='p'>,</span> <span class='n'>n_channel</span><span class='p'>,</span> <span class='n'>kernel_size</span><span class='o'>=</span><span class='mi'>3</span><span class='p'>)</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>bn2</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>BatchNorm1d</span><span class='p'>(</span><span class='n'>n_channel</span><span class='p'>)</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>pool2</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>MaxPool1d</span><span class='p'>(</span><span class='mi'>4</span><span class='p'>)</span>
 
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>conv3</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Conv1d</span><span class='p'>(</span><span class='n'>n_channel</span><span class='p'>,</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>n_channel</span><span class='p'>,</span> <span class='n'>kernel_size</span><span class='o'>=</span><span class='mi'>3</span><span class='p'>)</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>bn3</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>BatchNorm1d</span><span class='p'>(</span><span class='mi'>2</span> <span class='o'>*</span> <span class='n'>n_channel</span><span class='p'>)</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>pool3</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>MaxPool1d</span><span class='p'>(</span><span class='mi'>4</span><span class='p'>)</span>
+
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>conv4</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Conv1d</span><span class='p'>(</span><span class='mi'>2</span> <span class='o'>*</span> <span class='n'>n_channel</span><span class='p'>,</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>n_channel</span><span class='p'>,</span> <span class='n'>kernel_size</span><span class='o'>=</span><span class='mi'>3</span><span class='p'>)</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>bn4</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>BatchNorm1d</span><span class='p'>(</span><span class='mi'>2</span> <span class='o'>*</span> <span class='n'>n_channel</span><span class='p'>)</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>pool4</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>MaxPool1d</span><span class='p'>(</span><span class='mi'>4</span><span class='p'>)</span>
+
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>fc1</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Linear</span><span class='p'>(</span><span class='mi'>2</span> <span class='o'>*</span> <span class='n'>n_channel</span><span class='p'>,</span> <span class='n'>n_output</span><span class='p'>)</span></div>
+
+<div class='viewcode-block' id='M5.forward'><a class='viewcode-back' href='../../../../generated/modulation.classification.architecture.m5.M5.html#modulation.classification.architecture.m5.M5.forward'>[docs]</a>    <span class='k'>def</span> <span class='nf'>forward</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>x</span><span class='p'>):</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>        :param x:</span>
+<span class='sd'>        :type x:</span>
+<span class='sd'>        :return:</span>
+<span class='sd'>        :rtype:</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='n'>x</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>relu</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>bn1</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>conv1</span><span class='p'>(</span><span class='n'>x</span><span class='p'>)))</span>
+        <span class='n'>x</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>relu</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>bn2</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>conv2</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>pool1</span><span class='p'>(</span><span class='n'>x</span><span class='p'>))))</span>
+        <span class='n'>x</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>relu</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>bn3</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>conv3</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>pool2</span><span class='p'>(</span><span class='n'>x</span><span class='p'>))))</span>
+        <span class='n'>x</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>relu</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>bn4</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>conv4</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>pool3</span><span class='p'>(</span><span class='n'>x</span><span class='p'>))))</span>
+        <span class='n'>x</span> <span class='o'>=</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>pool4</span><span class='p'>(</span><span class='n'>x</span><span class='p'>)</span>
+
+        <span class='n'>x</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>avg_pool1d</span><span class='p'>(</span><span class='n'>x</span><span class='p'>,</span> <span class='n'>x</span><span class='o'>.</span><span class='n'>shape</span><span class='p'>[</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>])</span>
+
+        <span class='k'>return</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>log_softmax</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>fc1</span><span class='p'>(</span><span class='n'>x</span><span class='o'>.</span><span class='n'>permute</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='mi'>2</span><span class='p'>,</span> <span class='mi'>1</span><span class='p'>)),</span> <span class='n'>dim</span><span class='o'>=</span><span class='mi'>2</span><span class='p'>)</span></div></div>
+</pre>
+            </div>
+
+          </div>
 
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     <title>modulation.classification.metrics.lwlrap &#8212; modulation 0.0.1 documentation</title>
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-            
-  <h1>Source code for modulation.classification.metrics.lwlrap</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 03-12-2020</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">sklearn</span>
-
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;one_sample_positive_class_precisions&#39;</span><span class="p">,</span><span class="s1">&#39;lwlrap_accumulator&#39;</span><span class="p">,</span><span class="s1">&#39;calculate_per_class_lwlrap&#39;</span><span class="p">]</span>
-
-<div class="viewcode-block" id="one_sample_positive_class_precisions"><a class="viewcode-back" href="../../../../generated/modulation.classification.metrics.lwlrap.one_sample_positive_class_precisions.html#modulation.classification.metrics.lwlrap.one_sample_positive_class_precisions">[docs]</a><span class="k">def</span> <span class="nf">one_sample_positive_class_precisions</span><span class="p">(</span>
-    <span class="n">scores</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">,</span> <span class="n">truths</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    Reference implementation of l$\omega$lrap both natively and using sklearn.metrics.</span>
-
-<span class="sd">      Calculate precisions for each true class for a single sample.</span>
-
-<span class="sd">      Args:</span>
-<span class="sd">        scores: numpy.array of (num_classes,) giving the individual classifier scores.</span>
-<span class="sd">        truths: numpy.array of (num_classes,) bools indicating which classes are true.</span>
-
-<span class="sd">      Returns:</span>
-<span class="sd">        pos_class_indices: numpy.array of indices of the true classes for this sample.</span>
-<span class="sd">        pos_class_precisions: numpy.array of precisions corresponding to each of those</span>
-<span class="sd">          classes.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">pos_class_indices</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">flatnonzero</span><span class="p">(</span><span class="n">truths</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="nb">len</span><span class="p">(</span>
-        <span class="n">pos_class_indices</span>
-    <span class="p">):</span>  <span class="c1"># Only calculate precisions if there are some true classes.</span>
-        <span class="k">return</span> <span class="n">pos_class_indices</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-
-    <span class="n">retrieved_classes</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">scores</span><span class="p">)[</span>
-        <span class="p">::</span><span class="o">-</span><span class="mi">1</span>
-    <span class="p">]</span>  <span class="c1"># Retrieval list of classes for this sample.</span>
-
-    <span class="n">num_classes</span> <span class="o">=</span> <span class="n">scores</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">class_rankings</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span>
-        <span class="n">num_classes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int</span>
-    <span class="p">)</span>  <span class="c1"># class_rankings[top_scoring_class_index] == 0 etc.</span>
-    <span class="n">class_rankings</span><span class="p">[</span><span class="n">retrieved_classes</span><span class="p">]</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_classes</span><span class="p">)</span>
-
-    <span class="n">retrieved_class_true</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span>
-        <span class="n">num_classes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">bool</span>
-    <span class="p">)</span>  <span class="c1"># Which of these is a true label?</span>
-    <span class="n">retrieved_class_true</span><span class="p">[</span><span class="n">class_rankings</span><span class="p">[</span><span class="n">pos_class_indices</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">True</span>
-
-    <span class="n">retrieved_cumulative_hits</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span>
-        <span class="n">retrieved_class_true</span>
-    <span class="p">)</span>  <span class="c1"># Num hits for every truncated retrieval list.</span>
-    <span class="n">precision_at_hits</span> <span class="o">=</span> <span class="n">retrieved_cumulative_hits</span><span class="p">[</span>
-        <span class="c1"># Precision of retrieval list truncated at each hit, in order of pos_labels.</span>
-        <span class="n">class_rankings</span><span class="p">[</span><span class="n">pos_class_indices</span><span class="p">]</span>
-    <span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">class_rankings</span><span class="p">[</span><span class="n">pos_class_indices</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float</span><span class="p">))</span>
-
-    <span class="k">return</span> <span class="n">pos_class_indices</span><span class="p">,</span> <span class="n">precision_at_hits</span></div>
-
-
-<span class="k">def</span> <span class="nf">calculate_overall_lwlrap_sklearn</span><span class="p">(</span><span class="n">truth</span><span class="p">,</span> <span class="n">scores</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Calculate the overall lwlrap using sklearn.metrics.lrap.&quot;&quot;&quot;</span>
-    <span class="c1"># sklearn doesn&#39;t correctly apply weighting to samples with no labels, so just skip them.</span>
-    <span class="n">sample_weight</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">truth</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">nonzero_weight_sample_indices</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">flatnonzero</span><span class="p">(</span><span class="n">sample_weight</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span>
-    <span class="n">overall_lwlrap</span> <span class="o">=</span> <span class="n">sklearn</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">label_ranking_average_precision_score</span><span class="p">(</span>
-        <span class="n">truth</span><span class="p">[</span><span class="n">nonzero_weight_sample_indices</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span>
-        <span class="n">scores</span><span class="p">[</span><span class="n">nonzero_weight_sample_indices</span><span class="p">,</span> <span class="p">:],</span>
-        <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">[</span><span class="n">nonzero_weight_sample_indices</span><span class="p">],</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">overall_lwlrap</span>
-
-
-<div class="viewcode-block" id="lwlrap_accumulator"><a class="viewcode-back" href="../../../../generated/modulation.classification.metrics.lwlrap.lwlrap_accumulator.html#modulation.classification.metrics.lwlrap.lwlrap_accumulator">[docs]</a><span class="k">class</span> <span class="nc">lwlrap_accumulator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Accumulate batches of test samples into per-class and overall lwlrap.&quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="lwlrap_accumulator.__init__"><a class="viewcode-back" href="../../../../generated/modulation.classification.metrics.lwlrap.lwlrap_accumulator.html#modulation.classification.metrics.lwlrap.lwlrap_accumulator.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">total_num_samples</span> <span class="o">=</span> <span class="mi">0</span></div>
-
-<div class="viewcode-block" id="lwlrap_accumulator.accumulate_samples"><a class="viewcode-back" href="../../../../generated/modulation.classification.metrics.lwlrap.lwlrap_accumulator.html#modulation.classification.metrics.lwlrap.lwlrap_accumulator.accumulate_samples">[docs]</a>    <span class="k">def</span> <span class="nf">accumulate_samples</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_truth</span><span class="p">,</span> <span class="n">batch_scores</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Cumulate a new batch of samples into the metric.</span>
-
-<span class="sd">        Args:</span>
-<span class="sd">          truth: numpy.array of (num_samples, num_classes) giving boolean</span>
-<span class="sd">            ground-truth of presence of that class in that sample for this batch.</span>
-<span class="sd">          scores: numpy.array of (num_samples, num_classes) giving the</span>
-<span class="sd">            classifier-under-test&#39;s real-valued score for each class for each</span>
-<span class="sd">            sample.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">assert</span> <span class="n">batch_scores</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">batch_truth</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">num_samples</span><span class="p">,</span> <span class="n">num_classes</span> <span class="o">=</span> <span class="n">batch_truth</span><span class="o">.</span><span class="n">shape</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">=</span> <span class="n">num_classes</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_per_class_cumulative_precision</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_per_class_cumulative_count</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int</span>
-            <span class="p">)</span>
-        <span class="k">assert</span> <span class="n">num_classes</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span>
-        <span class="k">for</span> <span class="n">truth</span><span class="p">,</span> <span class="n">scores</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">batch_truth</span><span class="p">,</span> <span class="n">batch_scores</span><span class="p">):</span>
-            <span class="n">pos_class_indices</span><span class="p">,</span> <span class="n">precision_at_hits</span> <span class="o">=</span> <span class="n">one_sample_positive_class_precisions</span><span class="p">(</span>
-                <span class="n">scores</span><span class="p">,</span> <span class="n">truth</span>
-            <span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_per_class_cumulative_precision</span><span class="p">[</span><span class="n">pos_class_indices</span><span class="p">]</span> <span class="o">+=</span> <span class="n">precision_at_hits</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_per_class_cumulative_count</span><span class="p">[</span><span class="n">pos_class_indices</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">total_num_samples</span> <span class="o">+=</span> <span class="n">num_samples</span></div>
-
-<div class="viewcode-block" id="lwlrap_accumulator.per_class_lwlrap"><a class="viewcode-back" href="../../../../generated/modulation.classification.metrics.lwlrap.lwlrap_accumulator.html#modulation.classification.metrics.lwlrap.lwlrap_accumulator.per_class_lwlrap">[docs]</a>    <span class="k">def</span> <span class="nf">per_class_lwlrap</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return a vector of the per-class lwlraps for the accumulated samples.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_per_class_cumulative_precision</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span>
-            <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_per_class_cumulative_count</span>
-        <span class="p">)</span></div>
-
-<div class="viewcode-block" id="lwlrap_accumulator.per_class_weight"><a class="viewcode-back" href="../../../../generated/modulation.classification.metrics.lwlrap.lwlrap_accumulator.html#modulation.classification.metrics.lwlrap.lwlrap_accumulator.per_class_weight">[docs]</a>    <span class="k">def</span> <span class="nf">per_class_weight</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return a normalized weight vector for the contributions of each class.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_per_class_cumulative_count</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span>
-            <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_per_class_cumulative_count</span><span class="p">)</span>
-        <span class="p">)</span></div>
-
-<div class="viewcode-block" id="lwlrap_accumulator.overall_lwlrap"><a class="viewcode-back" href="../../../../generated/modulation.classification.metrics.lwlrap.lwlrap_accumulator.html#modulation.classification.metrics.lwlrap.lwlrap_accumulator.overall_lwlrap">[docs]</a>    <span class="k">def</span> <span class="nf">overall_lwlrap</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return the scalar overall lwlrap for cumulated samples.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">per_class_lwlrap</span><span class="p">()</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">per_class_weight</span><span class="p">())</span></div></div>
-
-
-
-
-<div class="viewcode-block" id="calculate_per_class_lwlrap"><a class="viewcode-back" href="../../../../generated/modulation.classification.metrics.lwlrap.calculate_per_class_lwlrap.html#modulation.classification.metrics.lwlrap.calculate_per_class_lwlrap">[docs]</a><span class="k">def</span> <span class="nf">calculate_per_class_lwlrap</span><span class="p">(</span><span class="n">truth</span><span class="p">,</span> <span class="n">scores</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Calculate label-weighted label-ranking average precision.</span>
-
-<span class="sd">    Arguments:</span>
-<span class="sd">      truth: numpy.array of (num_samples, num_classes) giving boolean ground-truth</span>
-<span class="sd">        of presence of that class in that sample.</span>
-<span class="sd">      scores: numpy.array of (num_samples, num_classes) giving the classifier-under-</span>
-<span class="sd">        test&#39;s real-valued score for each class for each sample.</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">      per_class_lwlrap: numpy.array of (num_classes,) giving the lwlrap for each</span>
-<span class="sd">        class.</span>
-<span class="sd">      weight_per_class: numpy.array of (num_classes,) giving the prior of each</span>
-<span class="sd">        class within the truth labels.  Then the overall unbalanced lwlrap is</span>
-<span class="sd">        simply numpy.sum(per_class_lwlrap * weight_per_class)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">assert</span> <span class="n">truth</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">scores</span><span class="o">.</span><span class="n">shape</span>
-    <span class="n">num_samples</span><span class="p">,</span> <span class="n">num_classes</span> <span class="o">=</span> <span class="n">scores</span><span class="o">.</span><span class="n">shape</span>
-    <span class="c1"># Space to store a distinct precision value for each class on each sample.</span>
-    <span class="c1"># Only the classes that are true for each sample will be filled in.</span>
-    <span class="n">precisions_for_samples_by_classes</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">num_samples</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">))</span>
-    <span class="k">for</span> <span class="n">sample_num</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_samples</span><span class="p">):</span>
-        <span class="n">pos_class_indices</span><span class="p">,</span> <span class="n">precision_at_hits</span> <span class="o">=</span> <span class="n">one_sample_positive_class_precisions</span><span class="p">(</span>
-            <span class="n">scores</span><span class="p">[</span><span class="n">sample_num</span><span class="p">,</span> <span class="p">:],</span> <span class="n">truth</span><span class="p">[</span><span class="n">sample_num</span><span class="p">,</span> <span class="p">:]</span>
-        <span class="p">)</span>
-        <span class="n">precisions_for_samples_by_classes</span><span class="p">[</span>
-            <span class="n">sample_num</span><span class="p">,</span> <span class="n">pos_class_indices</span>
-        <span class="p">]</span> <span class="o">=</span> <span class="n">precision_at_hits</span>
-    <span class="n">labels_per_class</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">truth</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">weight_per_class</span> <span class="o">=</span> <span class="n">labels_per_class</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">labels_per_class</span><span class="p">))</span>
-    <span class="c1"># Form average of each column, i.e. all the precisions assigned to labels in</span>
-    <span class="c1"># a particular class.</span>
-    <span class="n">per_class_lwlrap</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span>
-        <span class="n">precisions_for_samples_by_classes</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
-    <span class="p">)</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">labels_per_class</span><span class="p">)</span>
-    <span class="c1"># overall_lwlrap = simple average of all the actual per-class, per-sample precisions</span>
-    <span class="c1">#                = numpy.sum(precisions_for_samples_by_classes) / numpy.sum(precisions_for_samples_by_classes &gt; 0)</span>
-    <span class="c1">#           also = weighted mean of per-class lwlraps, weighted by class label prior across samples</span>
-    <span class="c1">#                = numpy.sum(per_class_lwlrap * weight_per_class)</span>
-    <span class="k">return</span> <span class="n">per_class_lwlrap</span><span class="p">,</span> <span class="n">weight_per_class</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-
-    <span class="k">def</span> <span class="nf">ijsaduh</span><span class="p">():</span>
-        <span class="c1"># Random test data.</span>
-        <span class="n">num_samples</span> <span class="o">=</span> <span class="mi">100</span>
-        <span class="n">num_labels</span> <span class="o">=</span> <span class="mi">20</span>
-
-        <span class="n">truth</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">num_samples</span><span class="p">,</span> <span class="n">num_labels</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mf">0.5</span>
-        <span class="c1"># Ensure at least some samples with no truth labels.</span>
-        <span class="n">truth</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="n">scores</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">num_samples</span><span class="p">,</span> <span class="n">num_labels</span><span class="p">)</span>
-
-        <span class="n">per_class_lwlrap</span><span class="p">,</span> <span class="n">weight_per_class</span> <span class="o">=</span> <span class="n">calculate_per_class_lwlrap</span><span class="p">(</span><span class="n">truth</span><span class="p">,</span> <span class="n">scores</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span>
-            <span class="s2">&quot;lwlrap from per-class values=&quot;</span><span class="p">,</span>
-            <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">per_class_lwlrap</span> <span class="o">*</span> <span class="n">weight_per_class</span><span class="p">),</span>
-        <span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span>
-            <span class="s2">&quot;lwlrap from sklearn.metrics =&quot;</span><span class="p">,</span>
-            <span class="n">calculate_overall_lwlrap_sklearn</span><span class="p">(</span><span class="n">truth</span><span class="p">,</span> <span class="n">scores</span><span class="p">),</span>
-        <span class="p">)</span>
-
-        <span class="c1"># Test of accumulator version.</span>
-        <span class="n">accumulator</span> <span class="o">=</span> <span class="n">lwlrap_accumulator</span><span class="p">()</span>
-        <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">12</span>
-        <span class="k">for</span> <span class="n">base_sample</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">scores</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">batch_size</span><span class="p">):</span>
-            <span class="n">accumulator</span><span class="o">.</span><span class="n">accumulate_samples</span><span class="p">(</span>
-                <span class="n">truth</span><span class="p">[</span><span class="n">base_sample</span> <span class="p">:</span> <span class="n">base_sample</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">,</span> <span class="p">:],</span>
-                <span class="n">scores</span><span class="p">[</span><span class="n">base_sample</span> <span class="p">:</span> <span class="n">base_sample</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">,</span> <span class="p">:],</span>
-            <span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;cumulative_lwlrap=&quot;</span><span class="p">,</span> <span class="n">accumulator</span><span class="o">.</span><span class="n">overall_lwlrap</span><span class="p">())</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;total_num_samples=&quot;</span><span class="p">,</span> <span class="n">accumulator</span><span class="o">.</span><span class="n">total_num_samples</span><span class="p">)</span>
-
-    <span class="n">ijsaduh</span><span class="p">()</span>
-</pre></div>
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+          <div class='body' role='main'>
 
+            <h1>Source code for modulation.classification.metrics.lwlrap</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
 
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 03-12-2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>import</span> <span class='nn'>sklearn</span>
+
+
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[</span><span class='s1'>&#39;one_sample_positive_class_precisions&#39;</span><span class='p'>,</span><span class='s1'>&#39;lwlrap_accumulator&#39;</span><span class='p'>,</span><span class='s1'>&#39;calculate_per_class_lwlrap&#39;</span><span class='p'>]</span>
+
+<div class='viewcode-block' id='one_sample_positive_class_precisions'><a class='viewcode-back' href='../../../../generated/modulation.classification.metrics.lwlrap.one_sample_positive_class_precisions.html#modulation.classification.metrics.lwlrap.one_sample_positive_class_precisions'>[docs]</a><span class='k'>def</span> <span class='nf'>one_sample_positive_class_precisions</span><span class='p'>(</span>
+    <span class='n'>scores</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>,</span> <span class='n'>truths</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    Reference implementation of l$\omega$lrap both natively and using sklearn.metrics.</span>
+
+<span class='sd'>      Calculate precisions for each true class for a single sample.</span>
+
+<span class='sd'>      Args:</span>
+<span class='sd'>        scores: numpy.array of (num_classes,) giving the individual classifier scores.</span>
+<span class='sd'>        truths: numpy.array of (num_classes,) bools indicating which classes are true.</span>
+
+<span class='sd'>      Returns:</span>
+<span class='sd'>        pos_class_indices: numpy.array of indices of the true classes for this sample.</span>
+<span class='sd'>        pos_class_precisions: numpy.array of precisions corresponding to each of those</span>
+<span class='sd'>          classes.</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+    <span class='n'>pos_class_indices</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>flatnonzero</span><span class='p'>(</span><span class='n'>truths</span> <span class='o'>&gt;</span> <span class='mi'>0</span><span class='p'>)</span>
 
+    <span class='k'>if</span> <span class='ow'>not</span> <span class='nb'>len</span><span class='p'>(</span>
+        <span class='n'>pos_class_indices</span>
+    <span class='p'>):</span>  <span class='c1'># Only calculate precisions if there are some true classes.</span>
+        <span class='k'>return</span> <span class='n'>pos_class_indices</span><span class='p'>,</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>zeros</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>)</span>
+
+    <span class='n'>retrieved_classes</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>argsort</span><span class='p'>(</span><span class='n'>scores</span><span class='p'>)[</span>
+        <span class='p'>::</span><span class='o'>-</span><span class='mi'>1</span>
+    <span class='p'>]</span>  <span class='c1'># Retrieval list of classes for this sample.</span>
+
+    <span class='n'>num_classes</span> <span class='o'>=</span> <span class='n'>scores</span><span class='o'>.</span><span class='n'>shape</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span>
+    <span class='n'>class_rankings</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>zeros</span><span class='p'>(</span>
+        <span class='n'>num_classes</span><span class='p'>,</span> <span class='n'>dtype</span><span class='o'>=</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>int</span>
+    <span class='p'>)</span>  <span class='c1'># class_rankings[top_scoring_class_index] == 0 etc.</span>
+    <span class='n'>class_rankings</span><span class='p'>[</span><span class='n'>retrieved_classes</span><span class='p'>]</span> <span class='o'>=</span> <span class='nb'>range</span><span class='p'>(</span><span class='n'>num_classes</span><span class='p'>)</span>
+
+    <span class='n'>retrieved_class_true</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>zeros</span><span class='p'>(</span>
+        <span class='n'>num_classes</span><span class='p'>,</span> <span class='n'>dtype</span><span class='o'>=</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>bool</span>
+    <span class='p'>)</span>  <span class='c1'># Which of these is a true label?</span>
+    <span class='n'>retrieved_class_true</span><span class='p'>[</span><span class='n'>class_rankings</span><span class='p'>[</span><span class='n'>pos_class_indices</span><span class='p'>]]</span> <span class='o'>=</span> <span class='kc'>True</span>
+
+    <span class='n'>retrieved_cumulative_hits</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>cumsum</span><span class='p'>(</span>
+        <span class='n'>retrieved_class_true</span>
+    <span class='p'>)</span>  <span class='c1'># Num hits for every truncated retrieval list.</span>
+    <span class='n'>precision_at_hits</span> <span class='o'>=</span> <span class='n'>retrieved_cumulative_hits</span><span class='p'>[</span>
+        <span class='c1'># Precision of retrieval list truncated at each hit, in order of pos_labels.</span>
+        <span class='n'>class_rankings</span><span class='p'>[</span><span class='n'>pos_class_indices</span><span class='p'>]</span>
+    <span class='p'>]</span> <span class='o'>/</span> <span class='p'>(</span><span class='mi'>1</span> <span class='o'>+</span> <span class='n'>class_rankings</span><span class='p'>[</span><span class='n'>pos_class_indices</span><span class='p'>]</span><span class='o'>.</span><span class='n'>astype</span><span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>float</span><span class='p'>))</span>
+
+    <span class='k'>return</span> <span class='n'>pos_class_indices</span><span class='p'>,</span> <span class='n'>precision_at_hits</span></div>
+
+
+<span class='k'>def</span> <span class='nf'>calculate_overall_lwlrap_sklearn</span><span class='p'>(</span><span class='n'>truth</span><span class='p'>,</span> <span class='n'>scores</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;Calculate the overall lwlrap using sklearn.metrics.lrap.&quot;&quot;&quot;</span>
+    <span class='c1'># sklearn doesn&#39;t correctly apply weighting to samples with no labels, so just skip them.</span>
+    <span class='n'>sample_weight</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sum</span><span class='p'>(</span><span class='n'>truth</span> <span class='o'>&gt;</span> <span class='mi'>0</span><span class='p'>,</span> <span class='n'>axis</span><span class='o'>=</span><span class='mi'>1</span><span class='p'>)</span>
+    <span class='n'>nonzero_weight_sample_indices</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>flatnonzero</span><span class='p'>(</span><span class='n'>sample_weight</span> <span class='o'>&gt;</span> <span class='mi'>0</span><span class='p'>)</span>
+    <span class='n'>overall_lwlrap</span> <span class='o'>=</span> <span class='n'>sklearn</span><span class='o'>.</span><span class='n'>metrics</span><span class='o'>.</span><span class='n'>label_ranking_average_precision_score</span><span class='p'>(</span>
+        <span class='n'>truth</span><span class='p'>[</span><span class='n'>nonzero_weight_sample_indices</span><span class='p'>,</span> <span class='p'>:]</span> <span class='o'>&gt;</span> <span class='mi'>0</span><span class='p'>,</span>
+        <span class='n'>scores</span><span class='p'>[</span><span class='n'>nonzero_weight_sample_indices</span><span class='p'>,</span> <span class='p'>:],</span>
+        <span class='n'>sample_weight</span><span class='o'>=</span><span class='n'>sample_weight</span><span class='p'>[</span><span class='n'>nonzero_weight_sample_indices</span><span class='p'>],</span>
+    <span class='p'>)</span>
+    <span class='k'>return</span> <span class='n'>overall_lwlrap</span>
+
+
+<div class='viewcode-block' id='lwlrap_accumulator'><a class='viewcode-back' href='../../../../generated/modulation.classification.metrics.lwlrap.lwlrap_accumulator.html#modulation.classification.metrics.lwlrap.lwlrap_accumulator'>[docs]</a><span class='k'>class</span> <span class='nc'>lwlrap_accumulator</span><span class='p'>(</span><span class='nb'>object</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;Accumulate batches of test samples into per-class and overall lwlrap.&quot;&quot;&quot;</span>
+
+<div class='viewcode-block' id='lwlrap_accumulator.__init__'><a class='viewcode-back' href='../../../../generated/modulation.classification.metrics.lwlrap.lwlrap_accumulator.html#modulation.classification.metrics.lwlrap.lwlrap_accumulator.__init__'>[docs]</a>    <span class='k'>def</span> <span class='fm'>__init__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>):</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>num_classes</span> <span class='o'>=</span> <span class='mi'>0</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>total_num_samples</span> <span class='o'>=</span> <span class='mi'>0</span></div>
+
+<div class='viewcode-block' id='lwlrap_accumulator.accumulate_samples'><a class='viewcode-back' href='../../../../generated/modulation.classification.metrics.lwlrap.lwlrap_accumulator.html#modulation.classification.metrics.lwlrap.lwlrap_accumulator.accumulate_samples'>[docs]</a>    <span class='k'>def</span> <span class='nf'>accumulate_samples</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>batch_truth</span><span class='p'>,</span> <span class='n'>batch_scores</span><span class='p'>):</span>
+        <span class='sd'>&quot;&quot;&quot;Cumulate a new batch of samples into the metric.</span>
+
+<span class='sd'>        Args:</span>
+<span class='sd'>          truth: numpy.array of (num_samples, num_classes) giving boolean</span>
+<span class='sd'>            ground-truth of presence of that class in that sample for this batch.</span>
+<span class='sd'>          scores: numpy.array of (num_samples, num_classes) giving the</span>
+<span class='sd'>            classifier-under-test&#39;s real-valued score for each class for each</span>
+<span class='sd'>            sample.</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='k'>assert</span> <span class='n'>batch_scores</span><span class='o'>.</span><span class='n'>shape</span> <span class='o'>==</span> <span class='n'>batch_truth</span><span class='o'>.</span><span class='n'>shape</span>
+        <span class='n'>num_samples</span><span class='p'>,</span> <span class='n'>num_classes</span> <span class='o'>=</span> <span class='n'>batch_truth</span><span class='o'>.</span><span class='n'>shape</span>
+        <span class='k'>if</span> <span class='ow'>not</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>num_classes</span><span class='p'>:</span>
+            <span class='bp'>self</span><span class='o'>.</span><span class='n'>num_classes</span> <span class='o'>=</span> <span class='n'>num_classes</span>
+            <span class='bp'>self</span><span class='o'>.</span><span class='n'>_per_class_cumulative_precision</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>zeros</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>num_classes</span><span class='p'>)</span>
+            <span class='bp'>self</span><span class='o'>.</span><span class='n'>_per_class_cumulative_count</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>zeros</span><span class='p'>(</span>
+                <span class='bp'>self</span><span class='o'>.</span><span class='n'>num_classes</span><span class='p'>,</span> <span class='n'>dtype</span><span class='o'>=</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>int</span>
+            <span class='p'>)</span>
+        <span class='k'>assert</span> <span class='n'>num_classes</span> <span class='o'>==</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>num_classes</span>
+        <span class='k'>for</span> <span class='n'>truth</span><span class='p'>,</span> <span class='n'>scores</span> <span class='ow'>in</span> <span class='nb'>zip</span><span class='p'>(</span><span class='n'>batch_truth</span><span class='p'>,</span> <span class='n'>batch_scores</span><span class='p'>):</span>
+            <span class='n'>pos_class_indices</span><span class='p'>,</span> <span class='n'>precision_at_hits</span> <span class='o'>=</span> <span class='n'>one_sample_positive_class_precisions</span><span class='p'>(</span>
+                <span class='n'>scores</span><span class='p'>,</span> <span class='n'>truth</span>
+            <span class='p'>)</span>
+            <span class='bp'>self</span><span class='o'>.</span><span class='n'>_per_class_cumulative_precision</span><span class='p'>[</span><span class='n'>pos_class_indices</span><span class='p'>]</span> <span class='o'>+=</span> <span class='n'>precision_at_hits</span>
+            <span class='bp'>self</span><span class='o'>.</span><span class='n'>_per_class_cumulative_count</span><span class='p'>[</span><span class='n'>pos_class_indices</span><span class='p'>]</span> <span class='o'>+=</span> <span class='mi'>1</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>total_num_samples</span> <span class='o'>+=</span> <span class='n'>num_samples</span></div>
+
+<div class='viewcode-block' id='lwlrap_accumulator.per_class_lwlrap'><a class='viewcode-back' href='../../../../generated/modulation.classification.metrics.lwlrap.lwlrap_accumulator.html#modulation.classification.metrics.lwlrap.lwlrap_accumulator.per_class_lwlrap'>[docs]</a>    <span class='k'>def</span> <span class='nf'>per_class_lwlrap</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>):</span>
+        <span class='sd'>&quot;&quot;&quot;Return a vector of the per-class lwlraps for the accumulated samples.&quot;&quot;&quot;</span>
+        <span class='k'>return</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>_per_class_cumulative_precision</span> <span class='o'>/</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>maximum</span><span class='p'>(</span>
+            <span class='mi'>1</span><span class='p'>,</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>_per_class_cumulative_count</span>
+        <span class='p'>)</span></div>
+
+<div class='viewcode-block' id='lwlrap_accumulator.per_class_weight'><a class='viewcode-back' href='../../../../generated/modulation.classification.metrics.lwlrap.lwlrap_accumulator.html#modulation.classification.metrics.lwlrap.lwlrap_accumulator.per_class_weight'>[docs]</a>    <span class='k'>def</span> <span class='nf'>per_class_weight</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>):</span>
+        <span class='sd'>&quot;&quot;&quot;Return a normalized weight vector for the contributions of each class.&quot;&quot;&quot;</span>
+        <span class='k'>return</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>_per_class_cumulative_count</span> <span class='o'>/</span> <span class='nb'>float</span><span class='p'>(</span>
+            <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sum</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>_per_class_cumulative_count</span><span class='p'>)</span>
+        <span class='p'>)</span></div>
+
+<div class='viewcode-block' id='lwlrap_accumulator.overall_lwlrap'><a class='viewcode-back' href='../../../../generated/modulation.classification.metrics.lwlrap.lwlrap_accumulator.html#modulation.classification.metrics.lwlrap.lwlrap_accumulator.overall_lwlrap'>[docs]</a>    <span class='k'>def</span> <span class='nf'>overall_lwlrap</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>):</span>
+        <span class='sd'>&quot;&quot;&quot;Return the scalar overall lwlrap for cumulated samples.&quot;&quot;&quot;</span>
+        <span class='k'>return</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sum</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>per_class_lwlrap</span><span class='p'>()</span> <span class='o'>*</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>per_class_weight</span><span class='p'>())</span></div></div>
+
+
+
+
+<div class='viewcode-block' id='calculate_per_class_lwlrap'><a class='viewcode-back' href='../../../../generated/modulation.classification.metrics.lwlrap.calculate_per_class_lwlrap.html#modulation.classification.metrics.lwlrap.calculate_per_class_lwlrap'>[docs]</a><span class='k'>def</span> <span class='nf'>calculate_per_class_lwlrap</span><span class='p'>(</span><span class='n'>truth</span><span class='p'>,</span> <span class='n'>scores</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;Calculate label-weighted label-ranking average precision.</span>
+
+<span class='sd'>    Arguments:</span>
+<span class='sd'>      truth: numpy.array of (num_samples, num_classes) giving boolean ground-truth</span>
+<span class='sd'>        of presence of that class in that sample.</span>
+<span class='sd'>      scores: numpy.array of (num_samples, num_classes) giving the classifier-under-</span>
+<span class='sd'>        test&#39;s real-valued score for each class for each sample.</span>
+
+<span class='sd'>    Returns:</span>
+<span class='sd'>      per_class_lwlrap: numpy.array of (num_classes,) giving the lwlrap for each</span>
+<span class='sd'>        class.</span>
+<span class='sd'>      weight_per_class: numpy.array of (num_classes,) giving the prior of each</span>
+<span class='sd'>        class within the truth labels.  Then the overall unbalanced lwlrap is</span>
+<span class='sd'>        simply numpy.sum(per_class_lwlrap * weight_per_class)</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>assert</span> <span class='n'>truth</span><span class='o'>.</span><span class='n'>shape</span> <span class='o'>==</span> <span class='n'>scores</span><span class='o'>.</span><span class='n'>shape</span>
+    <span class='n'>num_samples</span><span class='p'>,</span> <span class='n'>num_classes</span> <span class='o'>=</span> <span class='n'>scores</span><span class='o'>.</span><span class='n'>shape</span>
+    <span class='c1'># Space to store a distinct precision value for each class on each sample.</span>
+    <span class='c1'># Only the classes that are true for each sample will be filled in.</span>
+    <span class='n'>precisions_for_samples_by_classes</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>zeros</span><span class='p'>((</span><span class='n'>num_samples</span><span class='p'>,</span> <span class='n'>num_classes</span><span class='p'>))</span>
+    <span class='k'>for</span> <span class='n'>sample_num</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='n'>num_samples</span><span class='p'>):</span>
+        <span class='n'>pos_class_indices</span><span class='p'>,</span> <span class='n'>precision_at_hits</span> <span class='o'>=</span> <span class='n'>one_sample_positive_class_precisions</span><span class='p'>(</span>
+            <span class='n'>scores</span><span class='p'>[</span><span class='n'>sample_num</span><span class='p'>,</span> <span class='p'>:],</span> <span class='n'>truth</span><span class='p'>[</span><span class='n'>sample_num</span><span class='p'>,</span> <span class='p'>:]</span>
+        <span class='p'>)</span>
+        <span class='n'>precisions_for_samples_by_classes</span><span class='p'>[</span>
+            <span class='n'>sample_num</span><span class='p'>,</span> <span class='n'>pos_class_indices</span>
+        <span class='p'>]</span> <span class='o'>=</span> <span class='n'>precision_at_hits</span>
+    <span class='n'>labels_per_class</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sum</span><span class='p'>(</span><span class='n'>truth</span> <span class='o'>&gt;</span> <span class='mi'>0</span><span class='p'>,</span> <span class='n'>axis</span><span class='o'>=</span><span class='mi'>0</span><span class='p'>)</span>
+    <span class='n'>weight_per_class</span> <span class='o'>=</span> <span class='n'>labels_per_class</span> <span class='o'>/</span> <span class='nb'>float</span><span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>sum</span><span class='p'>(</span><span class='n'>labels_per_class</span><span class='p'>))</span>
+    <span class='c1'># Form average of each column, i.e. all the precisions assigned to labels in</span>
+    <span class='c1'># a particular class.</span>
+    <span class='n'>per_class_lwlrap</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sum</span><span class='p'>(</span>
+        <span class='n'>precisions_for_samples_by_classes</span><span class='p'>,</span> <span class='n'>axis</span><span class='o'>=</span><span class='mi'>0</span>
+    <span class='p'>)</span> <span class='o'>/</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>maximum</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>,</span> <span class='n'>labels_per_class</span><span class='p'>)</span>
+    <span class='c1'># overall_lwlrap = simple average of all the actual per-class, per-sample precisions</span>
+    <span class='c1'>#                = numpy.sum(precisions_for_samples_by_classes) / numpy.sum(precisions_for_samples_by_classes &gt; 0)</span>
+    <span class='c1'>#           also = weighted mean of per-class lwlraps, weighted by class label prior across samples</span>
+    <span class='c1'>#                = numpy.sum(per_class_lwlrap * weight_per_class)</span>
+    <span class='k'>return</span> <span class='n'>per_class_lwlrap</span><span class='p'>,</span> <span class='n'>weight_per_class</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+
+    <span class='k'>def</span> <span class='nf'>ijsaduh</span><span class='p'>():</span>
+        <span class='c1'># Random test data.</span>
+        <span class='n'>num_samples</span> <span class='o'>=</span> <span class='mi'>100</span>
+        <span class='n'>num_labels</span> <span class='o'>=</span> <span class='mi'>20</span>
+
+        <span class='n'>truth</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>random</span><span class='o'>.</span><span class='n'>rand</span><span class='p'>(</span><span class='n'>num_samples</span><span class='p'>,</span> <span class='n'>num_labels</span><span class='p'>)</span> <span class='o'>&gt;</span> <span class='mf'>0.5</span>
+        <span class='c1'># Ensure at least some samples with no truth labels.</span>
+        <span class='n'>truth</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>:</span><span class='mi'>1</span><span class='p'>,</span> <span class='p'>:]</span> <span class='o'>=</span> <span class='kc'>False</span>
+
+        <span class='n'>scores</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>random</span><span class='o'>.</span><span class='n'>rand</span><span class='p'>(</span><span class='n'>num_samples</span><span class='p'>,</span> <span class='n'>num_labels</span><span class='p'>)</span>
+
+        <span class='n'>per_class_lwlrap</span><span class='p'>,</span> <span class='n'>weight_per_class</span> <span class='o'>=</span> <span class='n'>calculate_per_class_lwlrap</span><span class='p'>(</span><span class='n'>truth</span><span class='p'>,</span> <span class='n'>scores</span><span class='p'>)</span>
+        <span class='nb'>print</span><span class='p'>(</span>
+            <span class='s2'>&quot;lwlrap from per-class values=&quot;</span><span class='p'>,</span>
+            <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sum</span><span class='p'>(</span><span class='n'>per_class_lwlrap</span> <span class='o'>*</span> <span class='n'>weight_per_class</span><span class='p'>),</span>
+        <span class='p'>)</span>
+        <span class='nb'>print</span><span class='p'>(</span>
+            <span class='s2'>&quot;lwlrap from sklearn.metrics =&quot;</span><span class='p'>,</span>
+            <span class='n'>calculate_overall_lwlrap_sklearn</span><span class='p'>(</span><span class='n'>truth</span><span class='p'>,</span> <span class='n'>scores</span><span class='p'>),</span>
+        <span class='p'>)</span>
+
+        <span class='c1'># Test of accumulator version.</span>
+        <span class='n'>accumulator</span> <span class='o'>=</span> <span class='n'>lwlrap_accumulator</span><span class='p'>()</span>
+        <span class='n'>batch_size</span> <span class='o'>=</span> <span class='mi'>12</span>
+        <span class='k'>for</span> <span class='n'>base_sample</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='n'>scores</span><span class='o'>.</span><span class='n'>shape</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>],</span> <span class='n'>batch_size</span><span class='p'>):</span>
+            <span class='n'>accumulator</span><span class='o'>.</span><span class='n'>accumulate_samples</span><span class='p'>(</span>
+                <span class='n'>truth</span><span class='p'>[</span><span class='n'>base_sample</span> <span class='p'>:</span> <span class='n'>base_sample</span> <span class='o'>+</span> <span class='n'>batch_size</span><span class='p'>,</span> <span class='p'>:],</span>
+                <span class='n'>scores</span><span class='p'>[</span><span class='n'>base_sample</span> <span class='p'>:</span> <span class='n'>base_sample</span> <span class='o'>+</span> <span class='n'>batch_size</span><span class='p'>,</span> <span class='p'>:],</span>
+            <span class='p'>)</span>
+        <span class='nb'>print</span><span class='p'>(</span><span class='s2'>&quot;cumulative_lwlrap=&quot;</span><span class='p'>,</span> <span class='n'>accumulator</span><span class='o'>.</span><span class='n'>overall_lwlrap</span><span class='p'>())</span>
+        <span class='nb'>print</span><span class='p'>(</span><span class='s2'>&quot;total_num_samples=&quot;</span><span class='p'>,</span> <span class='n'>accumulator</span><span class='o'>.</span><span class='n'>total_num_samples</span><span class='p'>)</span>
+
+    <span class='n'>ijsaduh</span><span class='p'>()</span>
+</pre>
+            </div>
 
+          </div>
 
         </div>
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     <title>modulation.classification.procedure &#8212; modulation 0.0.1 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
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-            
-  <h1>Source code for modulation.classification.procedure</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 02-12-2020</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">draugr.numpy_utilities</span> <span class="kn">import</span> <span class="n">SplitEnum</span>
-<span class="kn">from</span> <span class="nn">draugr.torch_utilities</span> <span class="kn">import</span> <span class="p">(</span>
-  <span class="n">TorchEvalSession</span><span class="p">,</span>
-  <span class="n">TorchTrainSession</span><span class="p">,</span>
-  <span class="n">global_torch_device</span><span class="p">,</span>
-  <span class="p">)</span>
-<span class="kn">from</span> <span class="nn">draugr.visualisation</span> <span class="kn">import</span> <span class="n">progress_bar</span>
-<span class="kn">from</span> <span class="nn">draugr.writers</span> <span class="kn">import</span> <span class="n">Writer</span>
-<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">Module</span>
-<span class="kn">from</span> <span class="nn">torch.nn.functional</span> <span class="kn">import</span> <span class="n">nll_loss</span>
-<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;single_epoch_fitting&#39;</span><span class="p">,</span> <span class="s1">&#39;single_epoch_evaluation&#39;</span><span class="p">]</span>
-
-
-<div class="viewcode-block" id="single_epoch_fitting"><a class="viewcode-back" href="../../../generated/modulation.classification.procedure.single_epoch_fitting.html#modulation.classification.procedure.single_epoch_fitting">[docs]</a><span class="k">def</span> <span class="nf">single_epoch_fitting</span><span class="p">(</span>
-    <span class="n">model</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
-    <span class="n">optimiser</span><span class="p">,</span>
-    <span class="n">train_loader_</span><span class="p">,</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">epoch</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">writer</span><span class="p">:</span> <span class="n">Writer</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">device_</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">global_torch_device</span><span class="p">(),</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-  <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">  :param model:</span>
-<span class="sd">  :type model:</span>
-<span class="sd">  :param optimiser:</span>
-<span class="sd">  :type optimiser:</span>
-<span class="sd">  :param train_loader_:</span>
-<span class="sd">  :type train_loader_:</span>
-<span class="sd">  :param epoch:</span>
-<span class="sd">  :type epoch:</span>
-<span class="sd">  :param writer:</span>
-<span class="sd">  :type writer:</span>
-<span class="sd">  :param device_:</span>
-<span class="sd">  :type device_:</span>
-<span class="sd">  &quot;&quot;&quot;</span>
-  <span class="n">accum_loss</span> <span class="o">=</span> <span class="mi">0</span>
-  <span class="n">num_batches</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_loader_</span><span class="p">)</span>
-
-  <span class="k">with</span> <span class="n">TorchTrainSession</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
-    <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="ow">in</span> <span class="n">progress_bar</span><span class="p">(</span>
-        <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_loader_</span><span class="p">),</span> <span class="n">description</span> <span class="o">=</span> <span class="s2">&quot;train batch #&quot;</span><span class="p">,</span> <span class="n">total</span> <span class="o">=</span> <span class="n">num_batches</span>
-        <span class="p">):</span>
-      <span class="n">loss</span> <span class="o">=</span> <span class="n">nll_loss</span><span class="p">(</span>
-          <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device_</span><span class="p">))</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="n">target</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device_</span><span class="p">)</span>
-          <span class="p">)</span>  <span class="c1"># negative log-likelihood for a tensor of size (batch x 1 x n_output)</span>
-      <span class="n">optimiser</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
-      <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
-      <span class="n">optimiser</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
-      <span class="n">accum_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
-
-  <span class="k">if</span> <span class="n">writer</span><span class="p">:</span>
-    <span class="n">writer</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="s2">&quot;loss&quot;</span><span class="p">,</span> <span class="n">accum_loss</span> <span class="o">/</span> <span class="n">num_batches</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="single_epoch_evaluation"><a class="viewcode-back" href="../../../generated/modulation.classification.procedure.single_epoch_evaluation.html#modulation.classification.procedure.single_epoch_evaluation">[docs]</a><span class="k">def</span> <span class="nf">single_epoch_evaluation</span><span class="p">(</span>
-    <span class="n">model</span><span class="p">:</span> <span class="n">Module</span><span class="p">,</span>
-    <span class="n">evaluation_loader</span><span class="p">:</span> <span class="n">DataLoader</span><span class="p">,</span>
-    <span class="n">subset</span><span class="p">:</span> <span class="n">SplitEnum</span><span class="p">,</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">epoch</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">writer</span><span class="p">:</span> <span class="n">Writer</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">device</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">global_torch_device</span><span class="p">(),</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-  <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">  :param model:</span>
-<span class="sd">  :type model:</span>
-<span class="sd">  :param evaluation_loader:</span>
-<span class="sd">  :type evaluation_loader:</span>
-<span class="sd">  :param subset:</span>
-<span class="sd">  :type subset:</span>
-<span class="sd">  :param epoch:</span>
-<span class="sd">  :type epoch:</span>
-<span class="sd">  :param writer:</span>
-<span class="sd">  :type writer:</span>
-<span class="sd">  :param device:</span>
-<span class="sd">  :type device:</span>
-<span class="sd">  :return:</span>
-<span class="sd">  :rtype:</span>
-<span class="sd">  &quot;&quot;&quot;</span>
-  <span class="n">correct</span> <span class="o">=</span> <span class="mi">0</span>
-  <span class="n">num_batches</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">evaluation_loader</span><span class="p">)</span>
-  <span class="k">with</span> <span class="n">TorchEvalSession</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
-    <span class="k">for</span> <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="ow">in</span> <span class="n">progress_bar</span><span class="p">(</span>
-        <span class="n">evaluation_loader</span><span class="p">,</span> <span class="n">description</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">subset</span><span class="si">}</span><span class="s2"> batch #&quot;</span><span class="p">,</span> <span class="n">total</span> <span class="o">=</span> <span class="n">num_batches</span>
-        <span class="p">):</span>
-      <span class="n">correct</span> <span class="o">+=</span> <span class="p">(</span>
-          <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">))</span>
-          <span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">dim</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-          <span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-          <span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">))</span>
-          <span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-          <span class="o">.</span><span class="n">item</span><span class="p">()</span>
-      <span class="p">)</span>
-
-  <span class="n">acc</span> <span class="o">=</span> <span class="n">correct</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">evaluation_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">)</span>
-  <span class="k">if</span> <span class="n">writer</span><span class="p">:</span>
-    <span class="n">writer</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">subset</span><span class="si">}</span><span class="s2">_accuracy&quot;</span><span class="p">,</span> <span class="n">acc</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
-  <span class="k">return</span> <span class="n">acc</span></div>
-</pre></div>
+    <link href='../../../_static/pygments.css' rel='stylesheet' type='text/css'/>
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+    <script src='../../../_static/doctools.js'></script>
+    <link href='Aivclab.github.io/modulation/_modules/modulation/classification/procedure.html' rel='canonical'/>
+    <link href='../../../genindex.html' rel='index' title='Index'/>
+    <link href='../../../search.html' rel='search' title='Search'/>
+
+    <link href='../../../_static/custom.css' rel='stylesheet' type='text/css'/>
+
+
+    <meta content='width=device-width, initial-scale=0.9, maximum-scale=0.9' name='viewport'/>
+
+  </head>
+  <body>
+
+
+    <div class='document'>
+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
+
+
+          <div class='body' role='main'>
+
+            <h1>Source code for modulation.classification.procedure</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 02-12-2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>import</span> <span class='nn'>torch</span>
+<span class='kn'>from</span> <span class='nn'>draugr.numpy_utilities</span> <span class='kn'>import</span> <span class='n'>SplitEnum</span>
+<span class='kn'>from</span> <span class='nn'>draugr.torch_utilities</span> <span class='kn'>import</span> <span class='p'>(</span>
+  <span class='n'>TorchEvalSession</span><span class='p'>,</span>
+  <span class='n'>TorchTrainSession</span><span class='p'>,</span>
+  <span class='n'>global_torch_device</span><span class='p'>,</span>
+  <span class='p'>)</span>
+<span class='kn'>from</span> <span class='nn'>draugr.visualisation</span> <span class='kn'>import</span> <span class='n'>progress_bar</span>
+<span class='kn'>from</span> <span class='nn'>draugr.writers</span> <span class='kn'>import</span> <span class='n'>Writer</span>
+<span class='kn'>from</span> <span class='nn'>torch.nn</span> <span class='kn'>import</span> <span class='n'>Module</span>
+<span class='kn'>from</span> <span class='nn'>torch.nn.functional</span> <span class='kn'>import</span> <span class='n'>nll_loss</span>
+<span class='kn'>from</span> <span class='nn'>torch.utils.data</span> <span class='kn'>import</span> <span class='n'>DataLoader</span>
+
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[</span><span class='s1'>&#39;single_epoch_fitting&#39;</span><span class='p'>,</span> <span class='s1'>&#39;single_epoch_evaluation&#39;</span><span class='p'>]</span>
+
+
+<div class='viewcode-block' id='single_epoch_fitting'><a class='viewcode-back' href='../../../generated/modulation.classification.procedure.single_epoch_fitting.html#modulation.classification.procedure.single_epoch_fitting'>[docs]</a><span class='k'>def</span> <span class='nf'>single_epoch_fitting</span><span class='p'>(</span>
+    <span class='n'>model</span><span class='p'>:</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Module</span><span class='p'>,</span>
+    <span class='n'>optimiser</span><span class='p'>,</span>
+    <span class='n'>train_loader_</span><span class='p'>,</span>
+    <span class='o'>*</span><span class='p'>,</span>
+    <span class='n'>epoch</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>,</span>
+    <span class='n'>writer</span><span class='p'>:</span> <span class='n'>Writer</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>,</span>
+    <span class='n'>device_</span><span class='p'>:</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>device</span> <span class='o'>=</span> <span class='n'>global_torch_device</span><span class='p'>(),</span>
+    <span class='p'>)</span> <span class='o'>-&gt;</span> <span class='kc'>None</span><span class='p'>:</span>
+  <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>  :param model:</span>
+<span class='sd'>  :type model:</span>
+<span class='sd'>  :param optimiser:</span>
+<span class='sd'>  :type optimiser:</span>
+<span class='sd'>  :param train_loader_:</span>
+<span class='sd'>  :type train_loader_:</span>
+<span class='sd'>  :param epoch:</span>
+<span class='sd'>  :type epoch:</span>
+<span class='sd'>  :param writer:</span>
+<span class='sd'>  :type writer:</span>
+<span class='sd'>  :param device_:</span>
+<span class='sd'>  :type device_:</span>
+<span class='sd'>  &quot;&quot;&quot;</span>
+  <span class='n'>accum_loss</span> <span class='o'>=</span> <span class='mi'>0</span>
+  <span class='n'>num_batches</span> <span class='o'>=</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>train_loader_</span><span class='p'>)</span>
+
+  <span class='k'>with</span> <span class='n'>TorchTrainSession</span><span class='p'>(</span><span class='n'>model</span><span class='p'>):</span>
+    <span class='k'>for</span> <span class='n'>batch_idx</span><span class='p'>,</span> <span class='p'>(</span><span class='n'>data</span><span class='p'>,</span> <span class='n'>target</span><span class='p'>)</span> <span class='ow'>in</span> <span class='n'>progress_bar</span><span class='p'>(</span>
+        <span class='nb'>enumerate</span><span class='p'>(</span><span class='n'>train_loader_</span><span class='p'>),</span> <span class='n'>description</span> <span class='o'>=</span> <span class='s2'>&quot;train batch #&quot;</span><span class='p'>,</span> <span class='n'>total</span> <span class='o'>=</span> <span class='n'>num_batches</span>
+        <span class='p'>):</span>
+      <span class='n'>loss</span> <span class='o'>=</span> <span class='n'>nll_loss</span><span class='p'>(</span>
+          <span class='n'>model</span><span class='p'>(</span><span class='n'>data</span><span class='o'>.</span><span class='n'>to</span><span class='p'>(</span><span class='n'>device_</span><span class='p'>))</span><span class='o'>.</span><span class='n'>squeeze</span><span class='p'>(),</span> <span class='n'>target</span><span class='o'>.</span><span class='n'>to</span><span class='p'>(</span><span class='n'>device_</span><span class='p'>)</span>
+          <span class='p'>)</span>  <span class='c1'># negative log-likelihood for a tensor of size (batch x 1 x n_output)</span>
+      <span class='n'>optimiser</span><span class='o'>.</span><span class='n'>zero_grad</span><span class='p'>()</span>
+      <span class='n'>loss</span><span class='o'>.</span><span class='n'>backward</span><span class='p'>()</span>
+      <span class='n'>optimiser</span><span class='o'>.</span><span class='n'>step</span><span class='p'>()</span>
+      <span class='n'>accum_loss</span> <span class='o'>+=</span> <span class='n'>loss</span><span class='o'>.</span><span class='n'>item</span><span class='p'>()</span>
+
+  <span class='k'>if</span> <span class='n'>writer</span><span class='p'>:</span>
+    <span class='n'>writer</span><span class='o'>.</span><span class='n'>scalar</span><span class='p'>(</span><span class='s2'>&quot;loss&quot;</span><span class='p'>,</span> <span class='n'>accum_loss</span> <span class='o'>/</span> <span class='n'>num_batches</span><span class='p'>,</span> <span class='n'>epoch</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='single_epoch_evaluation'><a class='viewcode-back' href='../../../generated/modulation.classification.procedure.single_epoch_evaluation.html#modulation.classification.procedure.single_epoch_evaluation'>[docs]</a><span class='k'>def</span> <span class='nf'>single_epoch_evaluation</span><span class='p'>(</span>
+    <span class='n'>model</span><span class='p'>:</span> <span class='n'>Module</span><span class='p'>,</span>
+    <span class='n'>evaluation_loader</span><span class='p'>:</span> <span class='n'>DataLoader</span><span class='p'>,</span>
+    <span class='n'>subset</span><span class='p'>:</span> <span class='n'>SplitEnum</span><span class='p'>,</span>
+    <span class='o'>*</span><span class='p'>,</span>
+    <span class='n'>epoch</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>,</span>
+    <span class='n'>writer</span><span class='p'>:</span> <span class='n'>Writer</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>,</span>
+    <span class='n'>device</span><span class='p'>:</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>device</span> <span class='o'>=</span> <span class='n'>global_torch_device</span><span class='p'>(),</span>
+    <span class='p'>)</span> <span class='o'>-&gt;</span> <span class='nb'>float</span><span class='p'>:</span>
+  <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>  :param model:</span>
+<span class='sd'>  :type model:</span>
+<span class='sd'>  :param evaluation_loader:</span>
+<span class='sd'>  :type evaluation_loader:</span>
+<span class='sd'>  :param subset:</span>
+<span class='sd'>  :type subset:</span>
+<span class='sd'>  :param epoch:</span>
+<span class='sd'>  :type epoch:</span>
+<span class='sd'>  :param writer:</span>
+<span class='sd'>  :type writer:</span>
+<span class='sd'>  :param device:</span>
+<span class='sd'>  :type device:</span>
+<span class='sd'>  :return:</span>
+<span class='sd'>  :rtype:</span>
+<span class='sd'>  &quot;&quot;&quot;</span>
+  <span class='n'>correct</span> <span class='o'>=</span> <span class='mi'>0</span>
+  <span class='n'>num_batches</span> <span class='o'>=</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>evaluation_loader</span><span class='p'>)</span>
+  <span class='k'>with</span> <span class='n'>TorchEvalSession</span><span class='p'>(</span><span class='n'>model</span><span class='p'>):</span>
+    <span class='k'>for</span> <span class='n'>data</span><span class='p'>,</span> <span class='n'>target</span> <span class='ow'>in</span> <span class='n'>progress_bar</span><span class='p'>(</span>
+        <span class='n'>evaluation_loader</span><span class='p'>,</span> <span class='n'>description</span> <span class='o'>=</span> <span class='sa'>f</span><span class='s2'>&quot;</span><span class='si'>{</span><span class='n'>subset</span><span class='si'>}</span><span class='s2'> batch #&quot;</span><span class='p'>,</span> <span class='n'>total</span> <span class='o'>=</span> <span class='n'>num_batches</span>
+        <span class='p'>):</span>
+      <span class='n'>correct</span> <span class='o'>+=</span> <span class='p'>(</span>
+          <span class='n'>model</span><span class='p'>(</span><span class='n'>data</span><span class='o'>.</span><span class='n'>to</span><span class='p'>(</span><span class='n'>device</span><span class='p'>))</span>
+          <span class='o'>.</span><span class='n'>argmax</span><span class='p'>(</span><span class='n'>dim</span> <span class='o'>=</span> <span class='o'>-</span><span class='mi'>1</span><span class='p'>)</span>
+          <span class='o'>.</span><span class='n'>squeeze</span><span class='p'>()</span>
+          <span class='o'>.</span><span class='n'>eq</span><span class='p'>(</span><span class='n'>target</span><span class='o'>.</span><span class='n'>to</span><span class='p'>(</span><span class='n'>device</span><span class='p'>))</span>
+          <span class='o'>.</span><span class='n'>sum</span><span class='p'>()</span>
+          <span class='o'>.</span><span class='n'>item</span><span class='p'>()</span>
+      <span class='p'>)</span>
+
+  <span class='n'>acc</span> <span class='o'>=</span> <span class='n'>correct</span> <span class='o'>/</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>evaluation_loader</span><span class='o'>.</span><span class='n'>dataset</span><span class='p'>)</span>
+  <span class='k'>if</span> <span class='n'>writer</span><span class='p'>:</span>
+    <span class='n'>writer</span><span class='o'>.</span><span class='n'>scalar</span><span class='p'>(</span><span class='sa'>f</span><span class='s2'>&quot;</span><span class='si'>{</span><span class='n'>subset</span><span class='si'>}</span><span class='s2'>_accuracy&quot;</span><span class='p'>,</span> <span class='n'>acc</span><span class='p'>,</span> <span class='n'>epoch</span><span class='p'>)</span>
+  <span class='k'>return</span> <span class='n'>acc</span></div>
+</pre>
+            </div>
 
           </div>
-          
+
         </div>
       </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
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-
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-<h3>Navigation</h3>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../generated/modulation.html">modulation</a></li>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../getting_started.html">Getting Started</a></li>
-</ul>
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-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../../../index.html">Documentation overview</a><ul>
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-  <li><a href="../../modulation.html">modulation</a><ul>
-  </ul></li>
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-  </ul></li>
-</ul>
-</div>
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-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../../../search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
-      <input type="submit" value="Go" />
-    </form>
-    </div>
-</div>
-<script>document.getElementById('searchbox').style.display = "block"</script>
-
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+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <p class='logo'><a href='../../../index.html'>
+            <img alt='Logo' class='logo' src='../../../_static/mod.svg'/>
+          </a></p>
+          <h1 class='logo'><a href='../../../index.html'>modulation</a></h1>
+
+
+          <h3>Navigation</h3>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../generated/modulation.html'>modulation</a></li>
+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../../../index.html'>Documentation overview</a>
+                <ul>
+                  <li><a href='../../index.html'>Module code</a>
+                    <ul>
+                      <li><a href='../../modulation.html'>modulation</a>
+                        <ul>
+                        </ul>
+                      </li>
+                    </ul>
+                  </li>
+                </ul>
+              </li>
+            </ul>
+          </div>
+          <div id='searchbox' role='search' style='display: none'>
+            <h3 id='searchlabel'>Quick search</h3>
+            <div class='searchformwrapper'>
+              <form action='../../../search.html' class='search' method='get'>
+                <input aria-labelledby='searchlabel' autocapitalize='off' autocomplete='off' autocorrect='off' name='q' spellcheck='false' type='text'/>
+                <input type='submit' value='Go'/>
+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
 
 
         </div>
       </div>
-      <div class="clearer"></div>
+      <div class='clearer'></div>
     </div>
-    <div class="footer">
+    <div class='footer'>
       &copy;.
-      
+
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
-      
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
+
     </div>
 
-    
 
-    
   </body>
 </html>
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diff --git a/docs/github/html/_modules/modulation/classification/seperation/audio_unet.html b/docs/github/html/_modules/modulation/classification/seperation/audio_unet.html
index 0e02011..cb924b1 100644
--- a/docs/github/html/_modules/modulation/classification/seperation/audio_unet.html
+++ b/docs/github/html/_modules/modulation/classification/seperation/audio_unet.html
@@ -1,423 +1,417 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
     <title>modulation.classification.seperation.audio_unet &#8212; modulation 0.0.1 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../../_static/alabaster.css" />
-    <link rel="stylesheet" type="text/css" href="../../../../_static/graphviz.css" />
-    <script data-url_root="../../../../" id="documentation_options" src="../../../../_static/documentation_options.js"></script>
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-    <link rel="canonical" href="Aivclab.github.io/modulation/_modules/modulation/classification/seperation/audio_unet.html" />
-    <link rel="index" title="Index" href="../../../../genindex.html" />
-    <link rel="search" title="Search" href="../../../../search.html" />
-   
-  <link rel="stylesheet" href="../../../../_static/custom.css" type="text/css" />
-  
-  
-  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
-
-  </head><body>
-  
-
-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          
-
-          <div class="body" role="main">
-            
-  <h1>Source code for modulation.classification.seperation.audio_unet</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 29/03/2020</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Tuple</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">draugr.writers</span> <span class="kn">import</span> <span class="n">Writer</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-
-<span class="kn">from</span> <span class="nn">modulation.torch_utilities</span> <span class="kn">import</span> <span class="n">torch_transforms</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;UNetConvBlock&#39;</span><span class="p">,</span><span class="s1">&#39;UNetDeconvBlock&#39;</span><span class="p">,</span><span class="s1">&#39;UNet&#39;</span><span class="p">]</span>
-
-<div class="viewcode-block" id="UNetConvBlock"><a class="viewcode-back" href="../../../../generated/modulation.classification.seperation.audio_unet.UNetConvBlock.html#modulation.classification.seperation.audio_unet.UNetConvBlock">[docs]</a><span class="k">class</span> <span class="nc">UNetConvBlock</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="UNetConvBlock.__init__"><a class="viewcode-back" href="../../../../generated/modulation.classification.seperation.audio_unet.UNetConvBlock.html#modulation.classification.seperation.audio_unet.UNetConvBlock.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="n">in_channels</span><span class="p">,</span>
-                <span class="n">out_channels</span><span class="p">,</span>
-                <span class="n">kernel_size</span><span class="p">,</span>
-                <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="n">kernel_size</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span>
-            <span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
-        <span class="p">)</span></div>
-
-<div class="viewcode-block" id="UNetConvBlock.forward"><a class="viewcode-back" href="../../../../generated/modulation.classification.seperation.audio_unet.UNetConvBlock.html#modulation.classification.seperation.audio_unet.UNetConvBlock.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">        :param x:</span>
-<span class="sd">        :type x:</span>
-<span class="sd">        :return:</span>
-<span class="sd">        :rtype:</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="UNetDeconvBlock"><a class="viewcode-back" href="../../../../generated/modulation.classification.seperation.audio_unet.UNetDeconvBlock.html#modulation.classification.seperation.audio_unet.UNetDeconvBlock">[docs]</a><span class="k">class</span> <span class="nc">UNetDeconvBlock</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="UNetDeconvBlock.__init__"><a class="viewcode-back" href="../../../../generated/modulation.classification.seperation.audio_unet.UNetDeconvBlock.html#modulation.classification.seperation.audio_unet.UNetDeconvBlock.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-        <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-        <span class="n">kernel_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-        <span class="n">dropout</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-        <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">activation</span> <span class="o">==</span> <span class="s2">&quot;relu&quot;</span><span class="p">:</span>
-            <span class="n">activation_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
-        <span class="k">elif</span> <span class="n">activation</span> <span class="o">==</span> <span class="s2">&quot;sigmoid&quot;</span><span class="p">:</span>
-            <span class="n">activation_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sigmoid</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid activation in UNetDeconvBlock: </span><span class="si">{</span><span class="n">activation</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">dropout</span><span class="p">:</span>
-            <span class="n">dropout_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout2d</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">dropout_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Identity</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ConvTranspose2d</span><span class="p">(</span>
-                <span class="n">in_channels</span><span class="p">,</span>
-                <span class="n">out_channels</span><span class="p">,</span>
-                <span class="n">kernel_size</span><span class="p">,</span>
-                <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="n">kernel_size</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span>
-                <span class="n">output_padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-            <span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">),</span>
-            <span class="n">dropout_layer</span><span class="p">,</span>
-            <span class="n">activation_layer</span><span class="p">,</span>
-        <span class="p">)</span></div>
-
-<div class="viewcode-block" id="UNetDeconvBlock.forward"><a class="viewcode-back" href="../../../../generated/modulation.classification.seperation.audio_unet.UNetDeconvBlock.html#modulation.classification.seperation.audio_unet.UNetDeconvBlock.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">residual</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Concatenate x with residual and deconvolve</span>
-<span class="sd">        :param x: torch.tensor of shape (1, n_fft, seq_len)</span>
-<span class="sd">        :param residual: torch.tensor of shape (1, n_fft, seq_len) or None</span>
-<span class="sd">        :return:</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">residual</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">residual</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="UNet"><a class="viewcode-back" href="../../../../generated/modulation.classification.seperation.audio_unet.UNet.html#modulation.classification.seperation.audio_unet.UNet">[docs]</a><span class="k">class</span> <span class="nc">UNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    UNet for source separation from https://ejhumphrey.com/assets/pdf/jansson2017singing.pdf</span>
-<span class="sd">    :param conv_channels_list: List of number of channels in convolutional blocks</span>
-<span class="sd">    :param spec_transform: Spectrogram transform with proper parameters from core.transforms</span>
-<span class="sd">    :param inv_spec_transform: InverseSpectrogram transform with proper parameters from core.transforms</span>
-<span class="sd">    :param optimizer_lr: Learning rate for Adam optimizer</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="UNet.__init__"><a class="viewcode-back" href="../../../../generated/modulation.classification.seperation.audio_unet.UNet.html#modulation.classification.seperation.audio_unet.UNet.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">conv_channels_list</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">1024</span><span class="p">),</span>
-        <span class="n">kernel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
-        <span class="n">spec_transform</span><span class="p">:</span> <span class="n">torch_transforms</span><span class="o">.</span><span class="n">Spectrogram</span> <span class="o">=</span> <span class="n">torch_transforms</span><span class="o">.</span><span class="n">Spectrogram</span><span class="p">,</span>
-        <span class="n">inv_spec_transform</span><span class="p">:</span> <span class="n">torch_transforms</span><span class="o">.</span><span class="n">InverseSpectrogram</span> <span class="o">=</span> <span class="n">torch_transforms</span><span class="o">.</span><span class="n">InverseSpectrogram</span><span class="p">,</span>
-        <span class="n">optimizer_lr</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.001</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">spec_transform</span> <span class="o">=</span> <span class="n">spec_transform</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">inv_spec_transform</span> <span class="o">=</span> <span class="n">inv_spec_transform</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">optimizer_lr</span> <span class="o">=</span> <span class="n">optimizer_lr</span>
-
-        <span class="n">conv_blocks</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">in_c</span><span class="p">,</span> <span class="n">out_c</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">conv_channels_list</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">conv_channels_list</span><span class="p">):</span>
-            <span class="n">conv_blocks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">UNetConvBlock</span><span class="p">(</span><span class="n">in_c</span><span class="p">,</span> <span class="n">out_c</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv_blocks</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span><span class="n">conv_blocks</span><span class="p">)</span>
-
-        <span class="c1"># First block does not have residual</span>
-        <span class="n">deconv_blocks</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">in_c</span><span class="p">,</span> <span class="n">out_c</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span>
-            <span class="nb">zip</span><span class="p">(</span><span class="n">conv_channels_list</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">conv_channels_list</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="p">):</span>
-            <span class="n">in_c</span> <span class="o">=</span> <span class="n">in_c</span> <span class="o">*</span> <span class="mi">2</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">in_c</span>
-            <span class="n">dropout</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">3</span> <span class="k">else</span> <span class="mi">0</span>
-            <span class="n">activation</span> <span class="o">=</span> <span class="s2">&quot;relu&quot;</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">conv_channels_list</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">else</span> <span class="s2">&quot;sigmoid&quot;</span>
-            <span class="n">deconv_blocks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                <span class="n">UNetDeconvBlock</span><span class="p">(</span>
-                    <span class="n">in_c</span><span class="p">,</span> <span class="n">out_c</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="n">dropout</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">activation</span>
-                <span class="p">)</span>
-            <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">deconv_blocks</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span><span class="n">deconv_blocks</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="UNet.forward"><a class="viewcode-back" href="../../../../generated/modulation.classification.seperation.audio_unet.UNet.html#modulation.classification.seperation.audio_unet.UNet.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Calculates forward pass.</span>
-<span class="sd">        :param x: tensor of shape (bs, n_fft, spec_len) -- input spectrogram</span>
-<span class="sd">        :return: tensor of shape (bs, n_fft, seq_len) -- output mask</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># x = (x - x.min()) / x.max()</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">residuals</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_blocks</span><span class="p">:</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">block</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-            <span class="n">residuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="n">residuals</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1"># For first deconv block</span>
-        <span class="n">residuals</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">reversed</span><span class="p">(</span><span class="n">residuals</span><span class="p">))</span>
-
-        <span class="k">for</span> <span class="n">block</span><span class="p">,</span> <span class="n">residual</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">deconv_blocks</span><span class="p">,</span> <span class="n">residuals</span><span class="p">):</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">block</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">residual</span><span class="p">)</span>
-
-        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">x</span></div>
-
-    <span class="c1"># def inference(self, spectrogram):</span>
-    <span class="c1">#     &quot;&quot;&quot;</span>
-    <span class="c1">#     Calculates waveform from spectrogram</span>
-    <span class="c1">#     :param spectrogram:</span>
-    <span class="c1">#     :return: tensor of shape (bs, 1, seq_len) -- predicted waveform</span>
-    <span class="c1">#     &quot;&quot;&quot;</span>
-    <span class="c1">#     spectrogram = self.melspec_upsampler(spectrogram)</span>
-    <span class="c1">#     bs, _, seq_len = spectrogram.shape</span>
-    <span class="c1">#</span>
-    <span class="c1">#     cur_waveform = torch.zeros([bs, 1, 1], device=self.device)</span>
-    <span class="c1">#     while cur_waveform.shape[2] &lt; spectrogram.shape[2]:</span>
-    <span class="c1">#         print(f&quot;{cur_waveform.shape[2]}/{spectrogram.shape[2]}&quot;)</span>
-    <span class="c1">#         input_waveform = self.input_conv(cur_waveform[:, :, -self.receptive_field:])</span>
-    <span class="c1">#         cur_result = torch.zeros_like(input_waveform, device=self.device)</span>
-    <span class="c1">#         prev = input_waveform</span>
-    <span class="c1">#         for block in self.residual_blocks:</span>
-    <span class="c1">#             block_result = block(prev, spectrogram[:, :, max(0, cur_waveform.shape[2] -self.receptive_field):cur_waveform.shape[2]])</span>
-    <span class="c1">#             cur_result += block_result</span>
-    <span class="c1">#             block_result += prev</span>
-    <span class="c1">#             prev = block_result</span>
-    <span class="c1">#         cur_result = self.output_conv(cur_result)</span>
-    <span class="c1">#         cur_result = cur_result[:, :, [-1]].argmax(dim=1, keepdim=True)</span>
-    <span class="c1">#         cur_waveform = torch.cat([cur_waveform, cur_result], dim=2)</span>
-    <span class="c1">#</span>
-    <span class="c1">#     return cur_waveform</span>
-
-<div class="viewcode-block" id="UNet.step"><a class="viewcode-back" href="../../../../generated/modulation.classification.seperation.audio_unet.UNet.html#modulation.classification.seperation.audio_unet.UNet.step">[docs]</a>    <span class="k">def</span> <span class="nf">step</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">batch</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]],</span>
-        <span class="n">batch_idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-        <span class="n">inference</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
-        <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span>
-    <span class="p">]:</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Pass batch to network, calculate losses and return total loss with gt and predicted spectrograms</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">mixture</span><span class="p">,</span> <span class="n">no_vocals</span><span class="p">,</span> <span class="n">sr</span> <span class="o">=</span> <span class="n">batch</span>
-        <span class="n">mixture_mag</span><span class="p">,</span> <span class="n">mixture_phase</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spec_transform</span><span class="p">(</span><span class="n">mixture</span><span class="p">)</span>
-        <span class="n">no_vocals_mag</span><span class="p">,</span> <span class="n">no_vocals_phase</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spec_transform</span><span class="p">(</span><span class="n">no_vocals</span><span class="p">)</span>
-        <span class="n">pred_mag</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="bp">self</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">mixture_mag</span><span class="o">.</span><span class="n">masked_fill</span><span class="p">((</span><span class="n">mixture_mag</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">),</span> <span class="mf">1e-5</span><span class="p">)))</span>
-            <span class="o">*</span> <span class="n">mixture_mag</span>
-        <span class="p">)</span>
-
-        <span class="n">loss</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">L1Loss</span><span class="p">()(</span><span class="n">pred_mag</span><span class="p">,</span> <span class="n">no_vocals_mag</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="p">(</span>
-            <span class="n">loss</span><span class="p">,</span>
-            <span class="n">pred_mag</span><span class="p">,</span>
-            <span class="n">mixture_mag</span><span class="p">,</span>
-            <span class="n">mixture_phase</span><span class="p">,</span>
-            <span class="n">no_vocals_mag</span><span class="p">,</span>
-            <span class="n">no_vocals_phase</span><span class="p">,</span>
-            <span class="n">sr</span><span class="p">,</span>
-        <span class="p">)</span></div>
-
-<div class="viewcode-block" id="UNet.training_step"><a class="viewcode-back" href="../../../../generated/modulation.classification.seperation.audio_unet.UNet.html#modulation.classification.seperation.audio_unet.UNet.training_step">[docs]</a>    <span class="k">def</span> <span class="nf">training_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_idx</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">        :param batch:</span>
-<span class="sd">        :type batch:</span>
-<span class="sd">        :param batch_idx:</span>
-<span class="sd">        :type batch_idx:</span>
-<span class="sd">        :return:</span>
-<span class="sd">        :rtype:</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="p">(</span>
-            <span class="n">loss</span><span class="p">,</span>
-            <span class="o">*</span><span class="n">_</span><span class="p">,</span>
-        <span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="n">inference</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="s2">&quot;train_loss&quot;</span><span class="p">,</span> <span class="n">loss</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">loss</span></div>
-
-<div class="viewcode-block" id="UNet.validation_step"><a class="viewcode-back" href="../../../../generated/modulation.classification.seperation.audio_unet.UNet.html#modulation.classification.seperation.audio_unet.UNet.validation_step">[docs]</a>    <span class="k">def</span> <span class="nf">validation_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="n">metric_writer</span><span class="p">:</span> <span class="n">Writer</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">        :param batch:</span>
-<span class="sd">        :type batch:</span>
-<span class="sd">        :param batch_idx:</span>
-<span class="sd">        :type batch_idx:</span>
-<span class="sd">        :return:</span>
-<span class="sd">        :rtype:</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Calculate losses and results in training and inference modes</span>
-        <span class="p">(</span>
-            <span class="n">loss</span><span class="p">,</span>
-            <span class="n">pred_mag</span><span class="p">,</span>
-            <span class="n">mixture_mag</span><span class="p">,</span>
-            <span class="n">mixture_phase</span><span class="p">,</span>
-            <span class="n">no_vocals_mag</span><span class="p">,</span>
-            <span class="n">no_vocals_phase</span><span class="p">,</span>
-            <span class="n">sr</span><span class="p">,</span>
-        <span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="n">inference</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="c1"># pred_waveform = self.inv_spec_transform((pred_mag, mixture_phase))</span>
-        <span class="c1"># mixed_waveform = self.inv_spec_transform((mixture_mag, mixture_phase))</span>
-        <span class="c1"># instrument_waveform = self.inv_spec_transform((no_vocals_mag, no_vocals_phase))</span>
-
-        <span class="n">mixed_audio</span> <span class="o">=</span> <span class="p">[</span>
-            <span class="c1"># wandb.Audio(wav.detach().cpu(), sample_rate=sr[0]) for wav in mixed_waveform</span>
-        <span class="p">]</span>
-        <span class="n">pred_audio</span> <span class="o">=</span> <span class="p">[</span>
-            <span class="c1"># wandb.Audio(wav.detach().cpu(), sample_rate=sr[0]) for wav in pred_waveform</span>
-        <span class="p">]</span>
-        <span class="n">instrument_audio</span> <span class="o">=</span> <span class="p">[</span>
-            <span class="c1"># wandb.Audio(wav.detach().cpu(), sample_rate=sr[0]) for wav in instrument_waveform</span>
-        <span class="p">]</span>
-
-        <span class="n">metric_writer</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="s2">&quot;Mixed audio&quot;</span><span class="p">,</span> <span class="n">mixed_audio</span><span class="p">)</span>
-        <span class="n">metric_writer</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="s2">&quot;Predicted audio&quot;</span><span class="p">,</span> <span class="n">pred_audio</span><span class="p">)</span>
-        <span class="n">metric_writer</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="s2">&quot;True audio&quot;</span><span class="p">,</span> <span class="n">instrument_audio</span><span class="p">)</span>
-        <span class="n">metric_writer</span><span class="o">.</span><span class="n">scalar</span><span class="p">(</span><span class="s2">&quot;val_loss&quot;</span><span class="p">,</span> <span class="n">loss</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="p">(</span>
-            <span class="n">loss</span><span class="p">,</span>
-            <span class="n">pred_mag</span><span class="p">,</span>
-            <span class="n">mixture_mag</span><span class="p">,</span>
-            <span class="n">mixture_phase</span><span class="p">,</span>
-            <span class="n">no_vocals_mag</span><span class="p">,</span>
-            <span class="n">no_vocals_phase</span><span class="p">,</span>
-        <span class="p">)</span></div>
-
-    <span class="c1"># def validation_epoch_end(self, outputs):</span>
-    <span class="c1">#     val_loss, true_waveform, val_pred_waveform, spectrogram = outputs[-1]</span>
-    <span class="c1">#     inf_waveform = self.inference(spectrogram[:4])</span>
-    <span class="c1">#     inf_waveform = torchaudio.functional.mu_law_decoding(inf_waveform, self.n_mu_law).squeeze(1)</span>
-    <span class="c1">#</span>
-    <span class="c1">#     inf_audio = [wandb.Audio(wav.detach().cpu(), sample_rate=22050) for wav in inf_waveform]</span>
-    <span class="c1">#     self.logger.experiment.log({&quot;Inferenced audio&quot;: inf_audio}, commit=False)</span>
-
-<div class="viewcode-block" id="UNet.configure_optimizers"><a class="viewcode-back" href="../../../../generated/modulation.classification.seperation.audio_unet.UNet.html#modulation.classification.seperation.audio_unet.UNet.configure_optimizers">[docs]</a>    <span class="k">def</span> <span class="nf">configure_optimizers</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">        :return:</span>
-<span class="sd">        :rtype:</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer_lr</span><span class="p">)</span>
-        <span class="k">return</span> <span class="p">{</span>
-            <span class="s2">&quot;optimizer&quot;</span><span class="p">:</span> <span class="n">optimizer</span><span class="p">,</span>
-        <span class="p">}</span></div></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-    <span class="k">pass</span>
-    <span class="n">unet</span> <span class="o">=</span> <span class="n">UNet</span><span class="p">()</span>
-    <span class="n">unet</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-</pre></div>
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+
+            <h1>Source code for modulation.classification.seperation.audio_unet</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 29/03/2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>List</span><span class='p'>,</span> <span class='n'>Tuple</span>
+
+<span class='kn'>import</span> <span class='nn'>torch</span>
+<span class='kn'>from</span> <span class='nn'>draugr.writers</span> <span class='kn'>import</span> <span class='n'>Writer</span>
+<span class='kn'>from</span> <span class='nn'>torch</span> <span class='kn'>import</span> <span class='n'>nn</span>
+
+<span class='kn'>from</span> <span class='nn'>modulation.torch_utilities</span> <span class='kn'>import</span> <span class='n'>torch_transforms</span>
+
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[</span><span class='s1'>&#39;UNetConvBlock&#39;</span><span class='p'>,</span><span class='s1'>&#39;UNetDeconvBlock&#39;</span><span class='p'>,</span><span class='s1'>&#39;UNet&#39;</span><span class='p'>]</span>
+
+<div class='viewcode-block' id='UNetConvBlock'><a class='viewcode-back' href='../../../../generated/modulation.classification.seperation.audio_unet.UNetConvBlock.html#modulation.classification.seperation.audio_unet.UNetConvBlock'>[docs]</a><span class='k'>class</span> <span class='nc'>UNetConvBlock</span><span class='p'>(</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Module</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+
+<div class='viewcode-block' id='UNetConvBlock.__init__'><a class='viewcode-back' href='../../../../generated/modulation.classification.seperation.audio_unet.UNetConvBlock.html#modulation.classification.seperation.audio_unet.UNetConvBlock.__init__'>[docs]</a>    <span class='k'>def</span> <span class='fm'>__init__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>in_channels</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span> <span class='n'>out_channels</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span> <span class='n'>kernel_size</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>):</span>
+        <span class='nb'>super</span><span class='p'>()</span><span class='o'>.</span><span class='fm'>__init__</span><span class='p'>()</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>conv</span> <span class='o'>=</span> <span class='n'>nn</span><span class='o'>.</span><span class='n'>Sequential</span><span class='p'>(</span>
+            <span class='n'>nn</span><span class='o'>.</span><span class='n'>Conv2d</span><span class='p'>(</span>
+                <span class='n'>in_channels</span><span class='p'>,</span>
+                <span class='n'>out_channels</span><span class='p'>,</span>
+                <span class='n'>kernel_size</span><span class='p'>,</span>
+                <span class='n'>stride</span><span class='o'>=</span><span class='mi'>2</span><span class='p'>,</span>
+                <span class='n'>padding</span><span class='o'>=</span><span class='n'>kernel_size</span> <span class='o'>//</span> <span class='mi'>2</span><span class='p'>,</span>
+            <span class='p'>),</span>
+            <span class='n'>nn</span><span class='o'>.</span><span class='n'>BatchNorm2d</span><span class='p'>(</span><span class='n'>out_channels</span><span class='p'>),</span>
+            <span class='n'>nn</span><span class='o'>.</span><span class='n'>LeakyReLU</span><span class='p'>(</span><span class='mf'>0.2</span><span class='p'>),</span>
+        <span class='p'>)</span></div>
+
+<div class='viewcode-block' id='UNetConvBlock.forward'><a class='viewcode-back' href='../../../../generated/modulation.classification.seperation.audio_unet.UNetConvBlock.html#modulation.classification.seperation.audio_unet.UNetConvBlock.forward'>[docs]</a>    <span class='k'>def</span> <span class='nf'>forward</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>x</span><span class='p'>):</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>        :param x:</span>
+<span class='sd'>        :type x:</span>
+<span class='sd'>        :return:</span>
+<span class='sd'>        :rtype:</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='k'>return</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>conv</span><span class='p'>(</span><span class='n'>x</span><span class='p'>)</span></div></div>
+
+
+<div class='viewcode-block' id='UNetDeconvBlock'><a class='viewcode-back' href='../../../../generated/modulation.classification.seperation.audio_unet.UNetDeconvBlock.html#modulation.classification.seperation.audio_unet.UNetDeconvBlock'>[docs]</a><span class='k'>class</span> <span class='nc'>UNetDeconvBlock</span><span class='p'>(</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Module</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+
+<div class='viewcode-block' id='UNetDeconvBlock.__init__'><a class='viewcode-back' href='../../../../generated/modulation.classification.seperation.audio_unet.UNetDeconvBlock.html#modulation.classification.seperation.audio_unet.UNetDeconvBlock.__init__'>[docs]</a>    <span class='k'>def</span> <span class='fm'>__init__</span><span class='p'>(</span>
+        <span class='bp'>self</span><span class='p'>,</span>
+        <span class='n'>in_channels</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span>
+        <span class='n'>out_channels</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span>
+        <span class='n'>kernel_size</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span>
+        <span class='n'>dropout</span><span class='o'>=</span><span class='mi'>0</span><span class='p'>,</span>
+        <span class='n'>activation</span><span class='o'>=</span><span class='s2'>&quot;relu&quot;</span><span class='p'>,</span>
+    <span class='p'>):</span>
+        <span class='nb'>super</span><span class='p'>()</span><span class='o'>.</span><span class='fm'>__init__</span><span class='p'>()</span>
+        <span class='k'>if</span> <span class='n'>activation</span> <span class='o'>==</span> <span class='s2'>&quot;relu&quot;</span><span class='p'>:</span>
+            <span class='n'>activation_layer</span> <span class='o'>=</span> <span class='n'>nn</span><span class='o'>.</span><span class='n'>ReLU</span><span class='p'>()</span>
+        <span class='k'>elif</span> <span class='n'>activation</span> <span class='o'>==</span> <span class='s2'>&quot;sigmoid&quot;</span><span class='p'>:</span>
+            <span class='n'>activation_layer</span> <span class='o'>=</span> <span class='n'>nn</span><span class='o'>.</span><span class='n'>Sigmoid</span><span class='p'>()</span>
+        <span class='k'>else</span><span class='p'>:</span>
+            <span class='k'>raise</span> <span class='ne'>ValueError</span><span class='p'>(</span><span class='sa'>f</span><span class='s2'>&quot;Invalid activation in UNetDeconvBlock: </span><span class='si'>{</span><span class='n'>activation</span><span class='si'>}</span><span class='s2'>&quot;</span><span class='p'>)</span>
+        <span class='k'>if</span> <span class='n'>dropout</span><span class='p'>:</span>
+            <span class='n'>dropout_layer</span> <span class='o'>=</span> <span class='n'>nn</span><span class='o'>.</span><span class='n'>Dropout2d</span><span class='p'>()</span>
+        <span class='k'>else</span><span class='p'>:</span>
+            <span class='n'>dropout_layer</span> <span class='o'>=</span> <span class='n'>nn</span><span class='o'>.</span><span class='n'>Identity</span><span class='p'>()</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>conv</span> <span class='o'>=</span> <span class='n'>nn</span><span class='o'>.</span><span class='n'>Sequential</span><span class='p'>(</span>
+            <span class='n'>nn</span><span class='o'>.</span><span class='n'>ConvTranspose2d</span><span class='p'>(</span>
+                <span class='n'>in_channels</span><span class='p'>,</span>
+                <span class='n'>out_channels</span><span class='p'>,</span>
+                <span class='n'>kernel_size</span><span class='p'>,</span>
+                <span class='n'>stride</span><span class='o'>=</span><span class='mi'>2</span><span class='p'>,</span>
+                <span class='n'>padding</span><span class='o'>=</span><span class='n'>kernel_size</span> <span class='o'>//</span> <span class='mi'>2</span><span class='p'>,</span>
+                <span class='n'>output_padding</span><span class='o'>=</span><span class='mi'>1</span><span class='p'>,</span>
+            <span class='p'>),</span>
+            <span class='n'>nn</span><span class='o'>.</span><span class='n'>BatchNorm2d</span><span class='p'>(</span><span class='n'>out_channels</span><span class='p'>),</span>
+            <span class='n'>dropout_layer</span><span class='p'>,</span>
+            <span class='n'>activation_layer</span><span class='p'>,</span>
+        <span class='p'>)</span></div>
+
+<div class='viewcode-block' id='UNetDeconvBlock.forward'><a class='viewcode-back' href='../../../../generated/modulation.classification.seperation.audio_unet.UNetDeconvBlock.html#modulation.classification.seperation.audio_unet.UNetDeconvBlock.forward'>[docs]</a>    <span class='k'>def</span> <span class='nf'>forward</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>x</span><span class='p'>,</span> <span class='n'>residual</span><span class='o'>=</span><span class='kc'>None</span><span class='p'>):</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>        Concatenate x with residual and deconvolve</span>
+<span class='sd'>        :param x: torch.tensor of shape (1, n_fft, seq_len)</span>
+<span class='sd'>        :param residual: torch.tensor of shape (1, n_fft, seq_len) or None</span>
+<span class='sd'>        :return:</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='k'>if</span> <span class='n'>residual</span> <span class='ow'>is</span> <span class='ow'>not</span> <span class='kc'>None</span><span class='p'>:</span>
+            <span class='n'>x</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>cat</span><span class='p'>([</span><span class='n'>x</span><span class='p'>,</span> <span class='n'>residual</span><span class='p'>],</span> <span class='n'>dim</span><span class='o'>=</span><span class='mi'>1</span><span class='p'>)</span>
+        <span class='k'>return</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>conv</span><span class='p'>(</span><span class='n'>x</span><span class='p'>)</span></div></div>
+
+
+<div class='viewcode-block' id='UNet'><a class='viewcode-back' href='../../../../generated/modulation.classification.seperation.audio_unet.UNet.html#modulation.classification.seperation.audio_unet.UNet'>[docs]</a><span class='k'>class</span> <span class='nc'>UNet</span><span class='p'>(</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Module</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    UNet for source separation from https://ejhumphrey.com/assets/pdf/jansson2017singing.pdf</span>
+<span class='sd'>    :param conv_channels_list: List of number of channels in convolutional blocks</span>
+<span class='sd'>    :param spec_transform: Spectrogram transform with proper parameters from core.transforms</span>
+<span class='sd'>    :param inv_spec_transform: InverseSpectrogram transform with proper parameters from core.transforms</span>
+<span class='sd'>    :param optimizer_lr: Learning rate for Adam optimizer</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+<div class='viewcode-block' id='UNet.__init__'><a class='viewcode-back' href='../../../../generated/modulation.classification.seperation.audio_unet.UNet.html#modulation.classification.seperation.audio_unet.UNet.__init__'>[docs]</a>    <span class='k'>def</span> <span class='fm'>__init__</span><span class='p'>(</span>
+        <span class='bp'>self</span><span class='p'>,</span>
+        <span class='n'>conv_channels_list</span><span class='p'>:</span> <span class='n'>List</span><span class='p'>[</span><span class='nb'>int</span><span class='p'>]</span> <span class='o'>=</span> <span class='p'>(</span><span class='mi'>64</span><span class='p'>,</span> <span class='mi'>128</span><span class='p'>,</span> <span class='mi'>256</span><span class='p'>,</span> <span class='mi'>512</span><span class='p'>,</span> <span class='mi'>1024</span><span class='p'>),</span>
+        <span class='n'>kernel_size</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='mi'>3</span><span class='p'>,</span>
+        <span class='n'>spec_transform</span><span class='p'>:</span> <span class='n'>torch_transforms</span><span class='o'>.</span><span class='n'>Spectrogram</span> <span class='o'>=</span> <span class='n'>torch_transforms</span><span class='o'>.</span><span class='n'>Spectrogram</span><span class='p'>,</span>
+        <span class='n'>inv_spec_transform</span><span class='p'>:</span> <span class='n'>torch_transforms</span><span class='o'>.</span><span class='n'>InverseSpectrogram</span> <span class='o'>=</span> <span class='n'>torch_transforms</span><span class='o'>.</span><span class='n'>InverseSpectrogram</span><span class='p'>,</span>
+        <span class='n'>optimizer_lr</span><span class='p'>:</span> <span class='nb'>float</span> <span class='o'>=</span> <span class='mf'>0.001</span><span class='p'>,</span>
+    <span class='p'>)</span> <span class='o'>-&gt;</span> <span class='kc'>None</span><span class='p'>:</span>
+        <span class='nb'>super</span><span class='p'>()</span><span class='o'>.</span><span class='fm'>__init__</span><span class='p'>()</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>spec_transform</span> <span class='o'>=</span> <span class='n'>spec_transform</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>inv_spec_transform</span> <span class='o'>=</span> <span class='n'>inv_spec_transform</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>optimizer_lr</span> <span class='o'>=</span> <span class='n'>optimizer_lr</span>
+
+        <span class='n'>conv_blocks</span> <span class='o'>=</span> <span class='p'>[]</span>
+        <span class='k'>for</span> <span class='n'>in_c</span><span class='p'>,</span> <span class='n'>out_c</span> <span class='ow'>in</span> <span class='nb'>zip</span><span class='p'>([</span><span class='mi'>1</span><span class='p'>]</span> <span class='o'>+</span> <span class='n'>conv_channels_list</span><span class='p'>[:</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>],</span> <span class='n'>conv_channels_list</span><span class='p'>):</span>
+            <span class='n'>conv_blocks</span><span class='o'>.</span><span class='n'>append</span><span class='p'>(</span><span class='n'>UNetConvBlock</span><span class='p'>(</span><span class='n'>in_c</span><span class='p'>,</span> <span class='n'>out_c</span><span class='p'>,</span> <span class='n'>kernel_size</span><span class='p'>))</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>conv_blocks</span> <span class='o'>=</span> <span class='n'>nn</span><span class='o'>.</span><span class='n'>ModuleList</span><span class='p'>(</span><span class='n'>conv_blocks</span><span class='p'>)</span>
+
+        <span class='c1'># First block does not have residual</span>
+        <span class='n'>deconv_blocks</span> <span class='o'>=</span> <span class='p'>[]</span>
+        <span class='k'>for</span> <span class='n'>i</span><span class='p'>,</span> <span class='p'>(</span><span class='n'>in_c</span><span class='p'>,</span> <span class='n'>out_c</span><span class='p'>)</span> <span class='ow'>in</span> <span class='nb'>enumerate</span><span class='p'>(</span>
+            <span class='nb'>zip</span><span class='p'>(</span><span class='n'>conv_channels_list</span><span class='p'>[::</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>],</span> <span class='n'>conv_channels_list</span><span class='p'>[::</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>][</span><span class='mi'>1</span><span class='p'>:]</span> <span class='o'>+</span> <span class='p'>[</span><span class='mi'>1</span><span class='p'>])</span>
+        <span class='p'>):</span>
+            <span class='n'>in_c</span> <span class='o'>=</span> <span class='n'>in_c</span> <span class='o'>*</span> <span class='mi'>2</span> <span class='k'>if</span> <span class='n'>i</span> <span class='o'>&gt;</span> <span class='mi'>0</span> <span class='k'>else</span> <span class='n'>in_c</span>
+            <span class='n'>dropout</span> <span class='o'>=</span> <span class='mf'>0.5</span> <span class='k'>if</span> <span class='n'>i</span> <span class='o'>&lt;</span> <span class='mi'>3</span> <span class='k'>else</span> <span class='mi'>0</span>
+            <span class='n'>activation</span> <span class='o'>=</span> <span class='s2'>&quot;relu&quot;</span> <span class='k'>if</span> <span class='n'>i</span> <span class='o'>&lt;</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>conv_channels_list</span><span class='p'>)</span> <span class='o'>-</span> <span class='mi'>1</span> <span class='k'>else</span> <span class='s2'>&quot;sigmoid&quot;</span>
+            <span class='n'>deconv_blocks</span><span class='o'>.</span><span class='n'>append</span><span class='p'>(</span>
+                <span class='n'>UNetDeconvBlock</span><span class='p'>(</span>
+                    <span class='n'>in_c</span><span class='p'>,</span> <span class='n'>out_c</span><span class='p'>,</span> <span class='n'>kernel_size</span><span class='p'>,</span> <span class='n'>dropout</span><span class='o'>=</span><span class='n'>dropout</span><span class='p'>,</span> <span class='n'>activation</span><span class='o'>=</span><span class='n'>activation</span>
+                <span class='p'>)</span>
+            <span class='p'>)</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>deconv_blocks</span> <span class='o'>=</span> <span class='n'>nn</span><span class='o'>.</span><span class='n'>ModuleList</span><span class='p'>(</span><span class='n'>deconv_blocks</span><span class='p'>)</span></div>
+
+<div class='viewcode-block' id='UNet.forward'><a class='viewcode-back' href='../../../../generated/modulation.classification.seperation.audio_unet.UNet.html#modulation.classification.seperation.audio_unet.UNet.forward'>[docs]</a>    <span class='k'>def</span> <span class='nf'>forward</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>x</span><span class='p'>:</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>:</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>        Calculates forward pass.</span>
+<span class='sd'>        :param x: tensor of shape (bs, n_fft, spec_len) -- input spectrogram</span>
+<span class='sd'>        :return: tensor of shape (bs, n_fft, seq_len) -- output mask</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='c1'># x = (x - x.min()) / x.max()</span>
+        <span class='n'>x</span> <span class='o'>=</span> <span class='n'>x</span><span class='o'>.</span><span class='n'>unsqueeze</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>)</span>
+        <span class='n'>residuals</span> <span class='o'>=</span> <span class='p'>[]</span>
+        <span class='k'>for</span> <span class='n'>block</span> <span class='ow'>in</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>conv_blocks</span><span class='p'>:</span>
+            <span class='n'>x</span> <span class='o'>=</span> <span class='n'>block</span><span class='p'>(</span><span class='n'>x</span><span class='p'>)</span>
+            <span class='n'>residuals</span><span class='o'>.</span><span class='n'>append</span><span class='p'>(</span><span class='n'>x</span><span class='p'>)</span>
+        <span class='n'>residuals</span><span class='p'>[</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>]</span> <span class='o'>=</span> <span class='kc'>None</span>  <span class='c1'># For first deconv block</span>
+        <span class='n'>residuals</span> <span class='o'>=</span> <span class='nb'>list</span><span class='p'>(</span><span class='nb'>reversed</span><span class='p'>(</span><span class='n'>residuals</span><span class='p'>))</span>
+
+        <span class='k'>for</span> <span class='n'>block</span><span class='p'>,</span> <span class='n'>residual</span> <span class='ow'>in</span> <span class='nb'>zip</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>deconv_blocks</span><span class='p'>,</span> <span class='n'>residuals</span><span class='p'>):</span>
+            <span class='n'>x</span> <span class='o'>=</span> <span class='n'>block</span><span class='p'>(</span><span class='n'>x</span><span class='p'>,</span> <span class='n'>residual</span><span class='p'>)</span>
+
+        <span class='n'>x</span> <span class='o'>=</span> <span class='n'>x</span><span class='o'>.</span><span class='n'>squeeze</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>)</span>
+        <span class='k'>return</span> <span class='n'>x</span></div>
+
+    <span class='c1'># def inference(self, spectrogram):</span>
+    <span class='c1'>#     &quot;&quot;&quot;</span>
+    <span class='c1'>#     Calculates waveform from spectrogram</span>
+    <span class='c1'>#     :param spectrogram:</span>
+    <span class='c1'>#     :return: tensor of shape (bs, 1, seq_len) -- predicted waveform</span>
+    <span class='c1'>#     &quot;&quot;&quot;</span>
+    <span class='c1'>#     spectrogram = self.melspec_upsampler(spectrogram)</span>
+    <span class='c1'>#     bs, _, seq_len = spectrogram.shape</span>
+    <span class='c1'>#</span>
+    <span class='c1'>#     cur_waveform = torch.zeros([bs, 1, 1], device=self.device)</span>
+    <span class='c1'>#     while cur_waveform.shape[2] &lt; spectrogram.shape[2]:</span>
+    <span class='c1'>#         print(f&quot;{cur_waveform.shape[2]}/{spectrogram.shape[2]}&quot;)</span>
+    <span class='c1'>#         input_waveform = self.input_conv(cur_waveform[:, :, -self.receptive_field:])</span>
+    <span class='c1'>#         cur_result = torch.zeros_like(input_waveform, device=self.device)</span>
+    <span class='c1'>#         prev = input_waveform</span>
+    <span class='c1'>#         for block in self.residual_blocks:</span>
+    <span class='c1'>#             block_result = block(prev, spectrogram[:, :, max(0, cur_waveform.shape[2] -self.receptive_field):cur_waveform.shape[2]])</span>
+    <span class='c1'>#             cur_result += block_result</span>
+    <span class='c1'>#             block_result += prev</span>
+    <span class='c1'>#             prev = block_result</span>
+    <span class='c1'>#         cur_result = self.output_conv(cur_result)</span>
+    <span class='c1'>#         cur_result = cur_result[:, :, [-1]].argmax(dim=1, keepdim=True)</span>
+    <span class='c1'>#         cur_waveform = torch.cat([cur_waveform, cur_result], dim=2)</span>
+    <span class='c1'>#</span>
+    <span class='c1'>#     return cur_waveform</span>
+
+<div class='viewcode-block' id='UNet.step'><a class='viewcode-back' href='../../../../generated/modulation.classification.seperation.audio_unet.UNet.html#modulation.classification.seperation.audio_unet.UNet.step'>[docs]</a>    <span class='k'>def</span> <span class='nf'>step</span><span class='p'>(</span>
+        <span class='bp'>self</span><span class='p'>,</span>
+        <span class='n'>batch</span><span class='p'>:</span> <span class='n'>Tuple</span><span class='p'>[</span><span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>,</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>,</span> <span class='n'>List</span><span class='p'>[</span><span class='nb'>int</span><span class='p'>]],</span>
+        <span class='n'>batch_idx</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span>
+        <span class='n'>inference</span><span class='p'>:</span> <span class='nb'>bool</span><span class='p'>,</span>
+    <span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Tuple</span><span class='p'>[</span>
+        <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>,</span>
+        <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>,</span>
+        <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>,</span>
+        <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>,</span>
+        <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>,</span>
+        <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>,</span>
+        <span class='n'>List</span><span class='p'>[</span><span class='nb'>int</span><span class='p'>],</span>
+    <span class='p'>]:</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>        Pass batch to network, calculate losses and return total loss with gt and predicted spectrograms</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='n'>mixture</span><span class='p'>,</span> <span class='n'>no_vocals</span><span class='p'>,</span> <span class='n'>sr</span> <span class='o'>=</span> <span class='n'>batch</span>
+        <span class='n'>mixture_mag</span><span class='p'>,</span> <span class='n'>mixture_phase</span> <span class='o'>=</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>spec_transform</span><span class='p'>(</span><span class='n'>mixture</span><span class='p'>)</span>
+        <span class='n'>no_vocals_mag</span><span class='p'>,</span> <span class='n'>no_vocals_phase</span> <span class='o'>=</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>spec_transform</span><span class='p'>(</span><span class='n'>no_vocals</span><span class='p'>)</span>
+        <span class='n'>pred_mag</span> <span class='o'>=</span> <span class='p'>(</span>
+            <span class='bp'>self</span><span class='p'>(</span><span class='n'>torch</span><span class='o'>.</span><span class='n'>log</span><span class='p'>(</span><span class='n'>mixture_mag</span><span class='o'>.</span><span class='n'>masked_fill</span><span class='p'>((</span><span class='n'>mixture_mag</span> <span class='o'>&lt;=</span> <span class='mi'>0</span><span class='p'>),</span> <span class='mf'>1e-5</span><span class='p'>)))</span>
+            <span class='o'>*</span> <span class='n'>mixture_mag</span>
+        <span class='p'>)</span>
+
+        <span class='n'>loss</span> <span class='o'>=</span> <span class='n'>nn</span><span class='o'>.</span><span class='n'>L1Loss</span><span class='p'>()(</span><span class='n'>pred_mag</span><span class='p'>,</span> <span class='n'>no_vocals_mag</span><span class='p'>)</span>
+
+        <span class='k'>return</span> <span class='p'>(</span>
+            <span class='n'>loss</span><span class='p'>,</span>
+            <span class='n'>pred_mag</span><span class='p'>,</span>
+            <span class='n'>mixture_mag</span><span class='p'>,</span>
+            <span class='n'>mixture_phase</span><span class='p'>,</span>
+            <span class='n'>no_vocals_mag</span><span class='p'>,</span>
+            <span class='n'>no_vocals_phase</span><span class='p'>,</span>
+            <span class='n'>sr</span><span class='p'>,</span>
+        <span class='p'>)</span></div>
+
+<div class='viewcode-block' id='UNet.training_step'><a class='viewcode-back' href='../../../../generated/modulation.classification.seperation.audio_unet.UNet.html#modulation.classification.seperation.audio_unet.UNet.training_step'>[docs]</a>    <span class='k'>def</span> <span class='nf'>training_step</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>batch</span><span class='p'>,</span> <span class='n'>batch_idx</span><span class='p'>):</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>        :param batch:</span>
+<span class='sd'>        :type batch:</span>
+<span class='sd'>        :param batch_idx:</span>
+<span class='sd'>        :type batch_idx:</span>
+<span class='sd'>        :return:</span>
+<span class='sd'>        :rtype:</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='p'>(</span>
+            <span class='n'>loss</span><span class='p'>,</span>
+            <span class='o'>*</span><span class='n'>_</span><span class='p'>,</span>
+        <span class='p'>)</span> <span class='o'>=</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>step</span><span class='p'>(</span><span class='n'>batch</span><span class='p'>,</span> <span class='n'>batch_idx</span><span class='p'>,</span> <span class='n'>inference</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>)</span>
+
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>log</span><span class='p'>(</span><span class='s2'>&quot;train_loss&quot;</span><span class='p'>,</span> <span class='n'>loss</span><span class='p'>)</span>
+        <span class='k'>return</span> <span class='n'>loss</span></div>
+
+<div class='viewcode-block' id='UNet.validation_step'><a class='viewcode-back' href='../../../../generated/modulation.classification.seperation.audio_unet.UNet.html#modulation.classification.seperation.audio_unet.UNet.validation_step'>[docs]</a>    <span class='k'>def</span> <span class='nf'>validation_step</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>batch</span><span class='p'>,</span> <span class='n'>batch_idx</span><span class='p'>,</span> <span class='n'>metric_writer</span><span class='p'>:</span> <span class='n'>Writer</span><span class='p'>):</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>        :param batch:</span>
+<span class='sd'>        :type batch:</span>
+<span class='sd'>        :param batch_idx:</span>
+<span class='sd'>        :type batch_idx:</span>
+<span class='sd'>        :return:</span>
+<span class='sd'>        :rtype:</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='c1'># Calculate losses and results in training and inference modes</span>
+        <span class='p'>(</span>
+            <span class='n'>loss</span><span class='p'>,</span>
+            <span class='n'>pred_mag</span><span class='p'>,</span>
+            <span class='n'>mixture_mag</span><span class='p'>,</span>
+            <span class='n'>mixture_phase</span><span class='p'>,</span>
+            <span class='n'>no_vocals_mag</span><span class='p'>,</span>
+            <span class='n'>no_vocals_phase</span><span class='p'>,</span>
+            <span class='n'>sr</span><span class='p'>,</span>
+        <span class='p'>)</span> <span class='o'>=</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>step</span><span class='p'>(</span><span class='n'>batch</span><span class='p'>,</span> <span class='n'>batch_idx</span><span class='p'>,</span> <span class='n'>inference</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>)</span>
+        <span class='c1'># pred_waveform = self.inv_spec_transform((pred_mag, mixture_phase))</span>
+        <span class='c1'># mixed_waveform = self.inv_spec_transform((mixture_mag, mixture_phase))</span>
+        <span class='c1'># instrument_waveform = self.inv_spec_transform((no_vocals_mag, no_vocals_phase))</span>
+
+        <span class='n'>mixed_audio</span> <span class='o'>=</span> <span class='p'>[</span>
+            <span class='c1'># wandb.Audio(wav.detach().cpu(), sample_rate=sr[0]) for wav in mixed_waveform</span>
+        <span class='p'>]</span>
+        <span class='n'>pred_audio</span> <span class='o'>=</span> <span class='p'>[</span>
+            <span class='c1'># wandb.Audio(wav.detach().cpu(), sample_rate=sr[0]) for wav in pred_waveform</span>
+        <span class='p'>]</span>
+        <span class='n'>instrument_audio</span> <span class='o'>=</span> <span class='p'>[</span>
+            <span class='c1'># wandb.Audio(wav.detach().cpu(), sample_rate=sr[0]) for wav in instrument_waveform</span>
+        <span class='p'>]</span>
+
+        <span class='n'>metric_writer</span><span class='o'>.</span><span class='n'>scalar</span><span class='p'>(</span><span class='s2'>&quot;Mixed audio&quot;</span><span class='p'>,</span> <span class='n'>mixed_audio</span><span class='p'>)</span>
+        <span class='n'>metric_writer</span><span class='o'>.</span><span class='n'>scalar</span><span class='p'>(</span><span class='s2'>&quot;Predicted audio&quot;</span><span class='p'>,</span> <span class='n'>pred_audio</span><span class='p'>)</span>
+        <span class='n'>metric_writer</span><span class='o'>.</span><span class='n'>scalar</span><span class='p'>(</span><span class='s2'>&quot;True audio&quot;</span><span class='p'>,</span> <span class='n'>instrument_audio</span><span class='p'>)</span>
+        <span class='n'>metric_writer</span><span class='o'>.</span><span class='n'>scalar</span><span class='p'>(</span><span class='s2'>&quot;val_loss&quot;</span><span class='p'>,</span> <span class='n'>loss</span><span class='p'>)</span>
+
+        <span class='k'>return</span> <span class='p'>(</span>
+            <span class='n'>loss</span><span class='p'>,</span>
+            <span class='n'>pred_mag</span><span class='p'>,</span>
+            <span class='n'>mixture_mag</span><span class='p'>,</span>
+            <span class='n'>mixture_phase</span><span class='p'>,</span>
+            <span class='n'>no_vocals_mag</span><span class='p'>,</span>
+            <span class='n'>no_vocals_phase</span><span class='p'>,</span>
+        <span class='p'>)</span></div>
+
+    <span class='c1'># def validation_epoch_end(self, outputs):</span>
+    <span class='c1'>#     val_loss, true_waveform, val_pred_waveform, spectrogram = outputs[-1]</span>
+    <span class='c1'>#     inf_waveform = self.inference(spectrogram[:4])</span>
+    <span class='c1'>#     inf_waveform = torchaudio.functional.mu_law_decoding(inf_waveform, self.n_mu_law).squeeze(1)</span>
+    <span class='c1'>#</span>
+    <span class='c1'>#     inf_audio = [wandb.Audio(wav.detach().cpu(), sample_rate=22050) for wav in inf_waveform]</span>
+    <span class='c1'>#     self.logger.experiment.log({&quot;Inferenced audio&quot;: inf_audio}, commit=False)</span>
+
+<div class='viewcode-block' id='UNet.configure_optimizers'><a class='viewcode-back' href='../../../../generated/modulation.classification.seperation.audio_unet.UNet.html#modulation.classification.seperation.audio_unet.UNet.configure_optimizers'>[docs]</a>    <span class='k'>def</span> <span class='nf'>configure_optimizers</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='nb'>dict</span><span class='p'>:</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>        :return:</span>
+<span class='sd'>        :rtype:</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='n'>optimizer</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>optim</span><span class='o'>.</span><span class='n'>Adam</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>parameters</span><span class='p'>(),</span> <span class='n'>lr</span><span class='o'>=</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>optimizer_lr</span><span class='p'>)</span>
+        <span class='k'>return</span> <span class='p'>{</span>
+            <span class='s2'>&quot;optimizer&quot;</span><span class='p'>:</span> <span class='n'>optimizer</span><span class='p'>,</span>
+        <span class='p'>}</span></div></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+    <span class='k'>pass</span>
+    <span class='n'>unet</span> <span class='o'>=</span> <span class='n'>UNet</span><span class='p'>()</span>
+    <span class='n'>unet</span><span class='o'>.</span><span class='n'>train</span><span class='p'>()</span>
+</pre>
+            </div>
 
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+
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index f8c008a..89d3ca7 100644
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+++ b/docs/github/html/_modules/modulation/data/audio/speech/seperation/dsd100.html
@@ -1,200 +1,194 @@
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     <title>modulation.data.audio.speech.seperation.dsd100 &#8212; modulation 0.0.1 documentation</title>
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-            
-  <h1>Source code for modulation.data.audio.speech.seperation.dsd100</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">random</span>
-<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">torchaudio</span>
-
-
-<div class="viewcode-block" id="DSD100"><a class="viewcode-back" href="../../../../../../generated/modulation.data.audio.speech.seperation.dsd100.DSD100.html#modulation.data.audio.speech.seperation.dsd100.DSD100">[docs]</a><span class="k">class</span> <span class="nc">DSD100</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    DSD100 dataset for audio source separation (2-stem)</span>
-<span class="sd">    :param root: Path to the directory where the dataset is found.</span>
-<span class="sd">    :param transforms: audiomentations transform object for waveform transform. Must end with Spectrogram transform</span>
-<span class="sd">    :param use_cuda: If true, will move tensor to cuda before applying transforms. (default: False)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="DSD100.__init__"><a class="viewcode-back" href="../../../../../../generated/modulation.data.audio.speech.seperation.dsd100.DSD100.html#modulation.data.audio.speech.seperation.dsd100.DSD100.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">root</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">transforms</span><span class="p">,</span> <span class="n">crop_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_cuda</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">root</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">root</span><span class="p">)</span>
-        <span class="k">assert</span> <span class="n">root</span><span class="o">.</span><span class="n">is_dir</span><span class="p">(),</span> <span class="sa">f</span><span class="s2">&quot;Path does not exist or is not a directory: </span><span class="si">{</span><span class="n">root</span><span class="si">}</span><span class="s2">&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">transforms</span> <span class="o">=</span> <span class="n">transforms</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">crop_size</span> <span class="o">=</span> <span class="n">crop_size</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">use_cuda</span> <span class="o">=</span> <span class="n">use_cuda</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">paths</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="n">name</span><span class="p">:</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">root</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;*/**/</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">.wav&quot;</span><span class="p">))</span>
-            <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;mixture&quot;</span><span class="p">,</span> <span class="s2">&quot;vocals&quot;</span><span class="p">,</span> <span class="s2">&quot;drums&quot;</span><span class="p">,</span> <span class="s2">&quot;bass&quot;</span><span class="p">,</span> <span class="s2">&quot;other&quot;</span><span class="p">]</span>
-        <span class="p">}</span></div>
-
-    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="nb">int</span><span class="p">]:</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        :return: four tensors: magnitude and phase of mixture, magnitude and phase without vocals</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">crop_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">num_frames</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">crop_size</span>
-            <span class="n">info</span> <span class="o">=</span> <span class="n">torchaudio</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">paths</span><span class="p">[</span><span class="s2">&quot;mixture&quot;</span><span class="p">][</span><span class="n">idx</span><span class="p">]))[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">frame_offset</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randrange</span><span class="p">(</span><span class="n">info</span><span class="o">.</span><span class="n">length</span> <span class="o">//</span> <span class="n">info</span><span class="o">.</span><span class="n">channels</span> <span class="o">-</span> <span class="n">num_frames</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">num_frames</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-            <span class="n">frame_offset</span> <span class="o">=</span> <span class="mi">0</span>
-
-        <span class="n">mixture</span><span class="p">,</span> <span class="n">sr</span> <span class="o">=</span> <span class="n">torchaudio</span><span class="o">.</span><span class="n">load</span><span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">paths</span><span class="p">[</span><span class="s2">&quot;mixture&quot;</span><span class="p">][</span><span class="n">idx</span><span class="p">],</span> <span class="n">offset</span><span class="o">=</span><span class="n">frame_offset</span><span class="p">,</span> <span class="n">num_frames</span><span class="o">=</span><span class="n">num_frames</span>
-        <span class="p">)</span>
-        <span class="n">sources</span> <span class="o">=</span> <span class="p">[</span>
-            <span class="n">torchaudio</span><span class="o">.</span><span class="n">load</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">paths</span><span class="p">[</span><span class="n">source</span><span class="p">][</span><span class="n">idx</span><span class="p">],</span> <span class="n">offset</span><span class="o">=</span><span class="n">frame_offset</span><span class="p">,</span> <span class="n">num_frames</span><span class="o">=</span><span class="n">num_frames</span>
-            <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="k">for</span> <span class="n">source</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;vocals&quot;</span><span class="p">]</span>
-        <span class="p">]</span>
-        <span class="n">no_vocals</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">sources</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_cuda</span><span class="p">:</span>
-            <span class="n">mixture</span><span class="p">,</span> <span class="n">no_vocals</span> <span class="o">=</span> <span class="n">mixture</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">no_vocals</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
-        <span class="n">mixture</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transforms</span><span class="p">(</span><span class="n">samples</span><span class="o">=</span><span class="n">mixture</span><span class="p">,</span> <span class="n">sample_rate</span><span class="o">=</span><span class="n">sr</span><span class="p">)</span>
-        <span class="n">no_vocals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transforms</span><span class="p">(</span><span class="n">samples</span><span class="o">=</span><span class="n">no_vocals</span><span class="p">,</span> <span class="n">sample_rate</span><span class="o">=</span><span class="n">sr</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">mixture</span><span class="p">,</span> <span class="n">no_vocals</span><span class="p">,</span> <span class="n">sr</span>
-
-    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">paths</span><span class="p">[</span><span class="s2">&quot;mixture&quot;</span><span class="p">])</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-
-    <span class="k">def</span> <span class="nf">fasfasa</span><span class="p">():</span>
-        <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-        <span class="kn">import</span> <span class="nn">audiomentations</span>
-
-        <span class="n">crop_size</span> <span class="o">=</span> <span class="mi">98303</span>  <span class="c1"># 768 * 128 - 1</span>
-        <span class="n">transforms</span> <span class="o">=</span> <span class="n">audiomentations</span><span class="o">.</span><span class="n">Compose</span><span class="p">(</span>
-            <span class="p">[</span>
-                <span class="n">audiomentations</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToMono</span><span class="p">(),</span>
-                <span class="n">audiomentations</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">Squeeze</span><span class="p">(),</span>
-                <span class="n">audiomentations</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToNumpy</span><span class="p">(),</span>
-                <span class="n">audiomentations</span><span class="o">.</span><span class="n">TimeStretch</span><span class="p">(</span><span class="n">min_rate</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">max_rate</span><span class="o">=</span><span class="mf">1.3</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">),</span>
-                <span class="n">audiomentations</span><span class="o">.</span><span class="n">PitchShift</span><span class="p">(</span><span class="n">min_semitones</span><span class="o">=-</span><span class="mi">4</span><span class="p">,</span> <span class="n">max_semitones</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">),</span>
-                <span class="n">audiomentations</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTorch</span><span class="p">(),</span>
-            <span class="p">]</span>
-        <span class="p">)</span>
-        <span class="n">train_dataset</span> <span class="o">=</span> <span class="n">DSD100</span><span class="p">(</span>
-            <span class="n">root</span><span class="o">=</span><span class="n">Path</span><span class="o">.</span><span class="n">home</span><span class="p">()</span> <span class="o">/</span> <span class="s2">&quot;Data&quot;</span> <span class="o">/</span> <span class="s2">&quot;Audio&quot;</span> <span class="o">/</span> <span class="s2">&quot;DSD100&quot;</span><span class="p">,</span>
-            <span class="n">crop_size</span><span class="o">=</span><span class="n">crop_size</span><span class="p">,</span>
-            <span class="n">transforms</span><span class="o">=</span><span class="n">transforms</span><span class="p">,</span>
-        <span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span> <span class="n">train_dataset</span><span class="p">):</span>
-            <span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
-
-        <span class="n">fasfasa</span><span class="p">()</span>
-</pre></div>
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+            <h1>Source code for modulation.data.audio.speech.seperation.dsd100</h1>
+            <div class='highlight'><pre>
+<span></span><span class='kn'>import</span> <span class='nn'>random</span>
+<span class='kn'>from</span> <span class='nn'>pathlib</span> <span class='kn'>import</span> <span class='n'>Path</span>
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Tuple</span>
+
+<span class='kn'>import</span> <span class='nn'>torch</span>
+<span class='kn'>import</span> <span class='nn'>torchaudio</span>
+
+
+<div class='viewcode-block' id='DSD100'><a class='viewcode-back' href='../../../../../../generated/modulation.data.audio.speech.seperation.dsd100.DSD100.html#modulation.data.audio.speech.seperation.dsd100.DSD100'>[docs]</a><span class='k'>class</span> <span class='nc'>DSD100</span><span class='p'>(</span><span class='n'>torch</span><span class='o'>.</span><span class='n'>utils</span><span class='o'>.</span><span class='n'>data</span><span class='o'>.</span><span class='n'>Dataset</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    DSD100 dataset for audio source separation (2-stem)</span>
+<span class='sd'>    :param root: Path to the directory where the dataset is found.</span>
+<span class='sd'>    :param transforms: audiomentations transform object for waveform transform. Must end with Spectrogram transform</span>
+<span class='sd'>    :param use_cuda: If true, will move tensor to cuda before applying transforms. (default: False)</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+<div class='viewcode-block' id='DSD100.__init__'><a class='viewcode-back' href='../../../../../../generated/modulation.data.audio.speech.seperation.dsd100.DSD100.html#modulation.data.audio.speech.seperation.dsd100.DSD100.__init__'>[docs]</a>    <span class='k'>def</span> <span class='fm'>__init__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>root</span><span class='p'>:</span> <span class='nb'>str</span><span class='p'>,</span> <span class='n'>transforms</span><span class='p'>,</span> <span class='n'>crop_size</span><span class='o'>=</span><span class='kc'>None</span><span class='p'>,</span> <span class='n'>use_cuda</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='kc'>None</span><span class='p'>:</span>
+        <span class='n'>root</span> <span class='o'>=</span> <span class='n'>Path</span><span class='p'>(</span><span class='n'>root</span><span class='p'>)</span>
+        <span class='k'>assert</span> <span class='n'>root</span><span class='o'>.</span><span class='n'>is_dir</span><span class='p'>(),</span> <span class='sa'>f</span><span class='s2'>&quot;Path does not exist or is not a directory: </span><span class='si'>{</span><span class='n'>root</span><span class='si'>}</span><span class='s2'>&quot;</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>transforms</span> <span class='o'>=</span> <span class='n'>transforms</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>crop_size</span> <span class='o'>=</span> <span class='n'>crop_size</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>use_cuda</span> <span class='o'>=</span> <span class='n'>use_cuda</span>
+
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>paths</span> <span class='o'>=</span> <span class='p'>{</span>
+            <span class='n'>name</span><span class='p'>:</span> <span class='nb'>sorted</span><span class='p'>(</span><span class='n'>root</span><span class='o'>.</span><span class='n'>glob</span><span class='p'>(</span><span class='sa'>f</span><span class='s2'>&quot;*/**/</span><span class='si'>{</span><span class='n'>name</span><span class='si'>}</span><span class='s2'>.wav&quot;</span><span class='p'>))</span>
+            <span class='k'>for</span> <span class='n'>name</span> <span class='ow'>in</span> <span class='p'>[</span><span class='s2'>&quot;mixture&quot;</span><span class='p'>,</span> <span class='s2'>&quot;vocals&quot;</span><span class='p'>,</span> <span class='s2'>&quot;drums&quot;</span><span class='p'>,</span> <span class='s2'>&quot;bass&quot;</span><span class='p'>,</span> <span class='s2'>&quot;other&quot;</span><span class='p'>]</span>
+        <span class='p'>}</span></div>
+
+    <span class='k'>def</span> <span class='fm'>__getitem__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>idx</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Tuple</span><span class='p'>[</span><span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>,</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>,</span> <span class='nb'>int</span><span class='p'>]:</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>        :return: four tensors: magnitude and phase of mixture, magnitude and phase without vocals</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='k'>if</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>crop_size</span> <span class='ow'>is</span> <span class='ow'>not</span> <span class='kc'>None</span><span class='p'>:</span>
+            <span class='n'>num_frames</span> <span class='o'>=</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>crop_size</span>
+            <span class='n'>info</span> <span class='o'>=</span> <span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>info</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>paths</span><span class='p'>[</span><span class='s2'>&quot;mixture&quot;</span><span class='p'>][</span><span class='n'>idx</span><span class='p'>]))[</span><span class='mi'>0</span><span class='p'>]</span>
+            <span class='n'>frame_offset</span> <span class='o'>=</span> <span class='n'>random</span><span class='o'>.</span><span class='n'>randrange</span><span class='p'>(</span><span class='n'>info</span><span class='o'>.</span><span class='n'>length</span> <span class='o'>//</span> <span class='n'>info</span><span class='o'>.</span><span class='n'>channels</span> <span class='o'>-</span> <span class='n'>num_frames</span><span class='p'>)</span>
+        <span class='k'>else</span><span class='p'>:</span>
+            <span class='n'>num_frames</span> <span class='o'>=</span> <span class='o'>-</span><span class='mi'>1</span>
+            <span class='n'>frame_offset</span> <span class='o'>=</span> <span class='mi'>0</span>
+
+        <span class='n'>mixture</span><span class='p'>,</span> <span class='n'>sr</span> <span class='o'>=</span> <span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>load</span><span class='p'>(</span>
+            <span class='bp'>self</span><span class='o'>.</span><span class='n'>paths</span><span class='p'>[</span><span class='s2'>&quot;mixture&quot;</span><span class='p'>][</span><span class='n'>idx</span><span class='p'>],</span> <span class='n'>offset</span><span class='o'>=</span><span class='n'>frame_offset</span><span class='p'>,</span> <span class='n'>num_frames</span><span class='o'>=</span><span class='n'>num_frames</span>
+        <span class='p'>)</span>
+        <span class='n'>sources</span> <span class='o'>=</span> <span class='p'>[</span>
+            <span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>load</span><span class='p'>(</span>
+                <span class='bp'>self</span><span class='o'>.</span><span class='n'>paths</span><span class='p'>[</span><span class='n'>source</span><span class='p'>][</span><span class='n'>idx</span><span class='p'>],</span> <span class='n'>offset</span><span class='o'>=</span><span class='n'>frame_offset</span><span class='p'>,</span> <span class='n'>num_frames</span><span class='o'>=</span><span class='n'>num_frames</span>
+            <span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+            <span class='k'>for</span> <span class='n'>source</span> <span class='ow'>in</span> <span class='p'>[</span><span class='s2'>&quot;vocals&quot;</span><span class='p'>]</span>
+        <span class='p'>]</span>
+        <span class='n'>no_vocals</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>stack</span><span class='p'>(</span><span class='n'>sources</span><span class='p'>,</span> <span class='n'>dim</span><span class='o'>=</span><span class='mi'>0</span><span class='p'>)</span><span class='o'>.</span><span class='n'>sum</span><span class='p'>(</span><span class='n'>dim</span><span class='o'>=</span><span class='mi'>0</span><span class='p'>)</span>
+
+        <span class='k'>if</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>use_cuda</span><span class='p'>:</span>
+            <span class='n'>mixture</span><span class='p'>,</span> <span class='n'>no_vocals</span> <span class='o'>=</span> <span class='n'>mixture</span><span class='o'>.</span><span class='n'>cuda</span><span class='p'>(),</span> <span class='n'>no_vocals</span><span class='o'>.</span><span class='n'>cuda</span><span class='p'>()</span>
+        <span class='n'>mixture</span> <span class='o'>=</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>transforms</span><span class='p'>(</span><span class='n'>samples</span><span class='o'>=</span><span class='n'>mixture</span><span class='p'>,</span> <span class='n'>sample_rate</span><span class='o'>=</span><span class='n'>sr</span><span class='p'>)</span>
+        <span class='n'>no_vocals</span> <span class='o'>=</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>transforms</span><span class='p'>(</span><span class='n'>samples</span><span class='o'>=</span><span class='n'>no_vocals</span><span class='p'>,</span> <span class='n'>sample_rate</span><span class='o'>=</span><span class='n'>sr</span><span class='p'>)</span>
+        <span class='k'>return</span> <span class='n'>mixture</span><span class='p'>,</span> <span class='n'>no_vocals</span><span class='p'>,</span> <span class='n'>sr</span>
+
+    <span class='k'>def</span> <span class='fm'>__len__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='nb'>int</span><span class='p'>:</span>
+        <span class='k'>return</span> <span class='nb'>len</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>paths</span><span class='p'>[</span><span class='s2'>&quot;mixture&quot;</span><span class='p'>])</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+
+    <span class='k'>def</span> <span class='nf'>fasfasa</span><span class='p'>():</span>
+        <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+        <span class='kn'>import</span> <span class='nn'>audiomentations</span>
+
+        <span class='n'>crop_size</span> <span class='o'>=</span> <span class='mi'>98303</span>  <span class='c1'># 768 * 128 - 1</span>
+        <span class='n'>transforms</span> <span class='o'>=</span> <span class='n'>audiomentations</span><span class='o'>.</span><span class='n'>Compose</span><span class='p'>(</span>
+            <span class='p'>[</span>
+                <span class='n'>audiomentations</span><span class='o'>.</span><span class='n'>core</span><span class='o'>.</span><span class='n'>transforms</span><span class='o'>.</span><span class='n'>ToMono</span><span class='p'>(),</span>
+                <span class='n'>audiomentations</span><span class='o'>.</span><span class='n'>core</span><span class='o'>.</span><span class='n'>transforms</span><span class='o'>.</span><span class='n'>Squeeze</span><span class='p'>(),</span>
+                <span class='n'>audiomentations</span><span class='o'>.</span><span class='n'>core</span><span class='o'>.</span><span class='n'>transforms</span><span class='o'>.</span><span class='n'>ToNumpy</span><span class='p'>(),</span>
+                <span class='n'>audiomentations</span><span class='o'>.</span><span class='n'>TimeStretch</span><span class='p'>(</span><span class='n'>min_rate</span><span class='o'>=</span><span class='mf'>0.7</span><span class='p'>,</span> <span class='n'>max_rate</span><span class='o'>=</span><span class='mf'>1.3</span><span class='p'>,</span> <span class='n'>p</span><span class='o'>=</span><span class='mf'>0.5</span><span class='p'>),</span>
+                <span class='n'>audiomentations</span><span class='o'>.</span><span class='n'>PitchShift</span><span class='p'>(</span><span class='n'>min_semitones</span><span class='o'>=-</span><span class='mi'>4</span><span class='p'>,</span> <span class='n'>max_semitones</span><span class='o'>=</span><span class='mi'>4</span><span class='p'>,</span> <span class='n'>p</span><span class='o'>=</span><span class='mf'>0.5</span><span class='p'>),</span>
+                <span class='n'>audiomentations</span><span class='o'>.</span><span class='n'>core</span><span class='o'>.</span><span class='n'>transforms</span><span class='o'>.</span><span class='n'>ToTorch</span><span class='p'>(),</span>
+            <span class='p'>]</span>
+        <span class='p'>)</span>
+        <span class='n'>train_dataset</span> <span class='o'>=</span> <span class='n'>DSD100</span><span class='p'>(</span>
+            <span class='n'>root</span><span class='o'>=</span><span class='n'>Path</span><span class='o'>.</span><span class='n'>home</span><span class='p'>()</span> <span class='o'>/</span> <span class='s2'>&quot;Data&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Audio&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;DSD100&quot;</span><span class='p'>,</span>
+            <span class='n'>crop_size</span><span class='o'>=</span><span class='n'>crop_size</span><span class='p'>,</span>
+            <span class='n'>transforms</span><span class='o'>=</span><span class='n'>transforms</span><span class='p'>,</span>
+        <span class='p'>)</span>
+
+        <span class='k'>for</span> <span class='n'>i</span><span class='p'>,</span> <span class='n'>e</span> <span class='ow'>in</span> <span class='nb'>zip</span><span class='p'>(</span><span class='nb'>range</span><span class='p'>(</span><span class='mi'>10</span><span class='p'>),</span> <span class='n'>train_dataset</span><span class='p'>):</span>
+            <span class='nb'>print</span><span class='p'>(</span><span class='n'>e</span><span class='p'>)</span>
+
+        <span class='n'>fasfasa</span><span class='p'>()</span>
+</pre>
+            </div>
 
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+
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+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
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diff --git a/docs/github/html/_modules/modulation/data/signal_generator.html b/docs/github/html/_modules/modulation/data/signal_generator.html
index 939c072..a19b8b1 100644
--- a/docs/github/html/_modules/modulation/data/signal_generator.html
+++ b/docs/github/html/_modules/modulation/data/signal_generator.html
@@ -1,223 +1,217 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
     <title>modulation.data.signal_generator &#8212; modulation 0.0.1 documentation</title>
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-        <div class="bodywrapper">
-          
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-          <div class="body" role="main">
-            
-  <h1>Source code for modulation.data.signal_generator</h1><div class="highlight"><pre>
-<span></span><span class="c1"># !/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 29/03/2020</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">reduce</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Union</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">from</span> <span class="nn">warg</span> <span class="kn">import</span> <span class="n">Number</span><span class="p">,</span> <span class="n">identity</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;SignalGenerator&quot;</span><span class="p">,</span> <span class="s2">&quot;multifreq&quot;</span><span class="p">,</span> <span class="s2">&quot;triangle&quot;</span><span class="p">,</span> <span class="s2">&quot;sawtooth&quot;</span><span class="p">]</span>
-
-
-<div class="viewcode-block" id="multifreq"><a class="viewcode-back" href="../../../generated/modulation.data.signal_generator.multifreq.html#modulation.data.signal_generator.multifreq">[docs]</a><span class="k">def</span> <span class="nf">multifreq</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-    <span class="k">return</span> <span class="p">(</span>
-        <span class="mi">2</span>
-        <span class="o">+</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-        <span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">x</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-        <span class="o">-</span> <span class="mf">0.2</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="mi">5</span> <span class="o">*</span> <span class="n">x</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-    <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="triangle"><a class="viewcode-back" href="../../../generated/modulation.data.signal_generator.triangle.html#modulation.data.signal_generator.triangle">[docs]</a><span class="k">def</span> <span class="nf">triangle</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">section_length</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-
-    <span class="n">section0</span> <span class="o">=</span> <span class="n">x</span> <span class="o">&lt;</span> <span class="n">section_length</span>
-    <span class="n">section1</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">&gt;=</span> <span class="n">section_length</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">x</span> <span class="o">&lt;</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">section_length</span><span class="p">)</span>
-    <span class="n">section2</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">&gt;=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">section_length</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">x</span> <span class="o">&lt;</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">section_length</span><span class="p">)</span>
-    <span class="n">section3</span> <span class="o">=</span> <span class="n">x</span> <span class="o">&gt;=</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">section_length</span>
-    <span class="n">output</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-    <span class="n">output</span><span class="p">[</span><span class="n">section0</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">section0</span><span class="p">]</span>
-    <span class="n">output</span><span class="p">[</span><span class="n">section1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">section_length</span> <span class="o">-</span> <span class="n">x</span><span class="p">[</span><span class="n">section1</span><span class="p">]</span>
-    <span class="n">output</span><span class="p">[</span><span class="n">section2</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">section2</span><span class="p">]</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">section_length</span>
-    <span class="n">output</span><span class="p">[</span><span class="n">section3</span><span class="p">]</span> <span class="o">=</span> <span class="mi">4</span> <span class="o">*</span> <span class="n">section_length</span> <span class="o">-</span> <span class="n">x</span><span class="p">[</span><span class="n">section3</span><span class="p">]</span>
-    <span class="k">return</span> <span class="n">output</span></div>
-
-
-<div class="viewcode-block" id="sawtooth"><a class="viewcode-back" href="../../../generated/modulation.data.signal_generator.sawtooth.html#modulation.data.signal_generator.sawtooth">[docs]</a><span class="k">def</span> <span class="nf">sawtooth</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">section_length</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-
-    <span class="n">section0</span> <span class="o">=</span> <span class="n">x</span> <span class="o">&lt;</span> <span class="n">section_length</span>
-    <span class="n">section1</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">&gt;=</span> <span class="n">section_length</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">x</span> <span class="o">&lt;</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">section_length</span><span class="p">)</span>
-    <span class="n">section2</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">&gt;=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">section_length</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">x</span> <span class="o">&lt;</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">section_length</span><span class="p">)</span>
-    <span class="n">section3</span> <span class="o">=</span> <span class="n">x</span> <span class="o">&gt;=</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">section_length</span>
-    <span class="n">output</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-    <span class="n">output</span><span class="p">[</span><span class="n">section0</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">section0</span><span class="p">]</span>
-    <span class="n">output</span><span class="p">[</span><span class="n">section1</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">section1</span><span class="p">]</span> <span class="o">-</span> <span class="n">section_length</span>
-    <span class="n">output</span><span class="p">[</span><span class="n">section2</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">section2</span><span class="p">]</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">section_length</span>
-    <span class="n">output</span><span class="p">[</span><span class="n">section3</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">section3</span><span class="p">]</span> <span class="o">-</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">section_length</span>
-    <span class="k">return</span> <span class="n">output</span></div>
-
-
-<div class="viewcode-block" id="SignalGenerator"><a class="viewcode-back" href="../../../generated/modulation.data.signal_generator.SignalGenerator.html#modulation.data.signal_generator.SignalGenerator">[docs]</a><span class="k">class</span> <span class="nc">SignalGenerator</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="SignalGenerator.__init__"><a class="viewcode-back" href="../../../generated/modulation.data.signal_generator.SignalGenerator.html#modulation.data.signal_generator.SignalGenerator.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">funcs</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Callable</span><span class="p">,</span> <span class="n">Number</span><span class="p">],</span> <span class="n">delta_time</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">reset_internal_time</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">delta_time</span> <span class="o">=</span> <span class="n">delta_time</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">funcs</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">*</span><span class="n">funcs</span><span class="p">)</span></div>
-
-    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">reset_internal_time</span><span class="p">()</span>
-        <span class="k">return</span> <span class="bp">self</span>
-
-    <span class="k">def</span> <span class="fm">__next__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">t</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">delta_time</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">t</span><span class="p">)</span>
-
-<div class="viewcode-block" id="SignalGenerator.apply"><a class="viewcode-back" href="../../../generated/modulation.data.signal_generator.SignalGenerator.html#modulation.data.signal_generator.SignalGenerator.apply">[docs]</a>    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">        :param t:</span>
-<span class="sd">        :type t:</span>
-<span class="sd">        :return:</span>
-<span class="sd">        :rtype:</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">(</span><span class="n">t</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">funcs</span><span class="p">)</span></div>
+    <link href='../../../_static/pygments.css' rel='stylesheet' type='text/css'/>
+    <link href='../../../_static/alabaster.css' rel='stylesheet' type='text/css'/>
+    <link href='../../../_static/graphviz.css' rel='stylesheet' type='text/css'/>
+    <script data-url_root='../../../' id='documentation_options' src='../../../_static/documentation_options.js'></script>
+    <script src='../../../_static/jquery.js'></script>
+    <script src='../../../_static/underscore.js'></script>
+    <script src='../../../_static/_sphinx_javascript_frameworks_compat.js'></script>
+    <script src='../../../_static/doctools.js'></script>
+    <link href='Aivclab.github.io/modulation/_modules/modulation/data/signal_generator.html' rel='canonical'/>
+    <link href='../../../genindex.html' rel='index' title='Index'/>
+    <link href='../../../search.html' rel='search' title='Search'/>
 
-    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Number</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">Sequence</span><span class="p">:</span>
-        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">t</span><span class="p">]</span>
+    <link href='../../../_static/custom.css' rel='stylesheet' type='text/css'/>
 
-<div class="viewcode-block" id="SignalGenerator.reset_internal_time"><a class="viewcode-back" href="../../../generated/modulation.data.signal_generator.SignalGenerator.html#modulation.data.signal_generator.SignalGenerator.reset_internal_time">[docs]</a>    <span class="k">def</span> <span class="nf">reset_internal_time</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">t</span> <span class="o">=</span> <span class="mf">0.0</span></div>
 
-<div class="viewcode-block" id="SignalGenerator.set_internal_time"><a class="viewcode-back" href="../../../generated/modulation.data.signal_generator.SignalGenerator.html#modulation.data.signal_generator.SignalGenerator.set_internal_time">[docs]</a>    <span class="k">def</span> <span class="nf">set_internal_time</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">t</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
+    <meta content='width=device-width, initial-scale=0.9, maximum-scale=0.9' name='viewport'/>
 
-<span class="sd">        :param t:</span>
-<span class="sd">        :type t:</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">t</span> <span class="o">=</span> <span class="n">t</span></div>
+  </head>
+  <body>
 
-    <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">reset_internal_time</span><span class="p">()</span>
-        <span class="k">return</span> <span class="kc">True</span></div>
 
+    <div class='document'>
+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
 
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
 
-    <span class="k">def</span> <span class="nf">asidjashdya</span><span class="p">():</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        counts</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span> <span class="n">SignalGenerator</span><span class="p">(</span><span class="n">identity</span><span class="p">)):</span>
-            <span class="nb">print</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+          <div class='body' role='main'>
 
-    <span class="n">asidjashdya</span><span class="p">()</span>
-</pre></div>
+            <h1>Source code for modulation.data.signal_generator</h1>
+            <div class='highlight'><pre>
+<span></span><span class='c1'># !/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 29/03/2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>from</span> <span class='nn'>functools</span> <span class='kn'>import</span> <span class='n'>reduce</span>
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Callable</span><span class='p'>,</span> <span class='n'>Iterable</span><span class='p'>,</span> <span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>Union</span>
+
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>from</span> <span class='nn'>warg</span> <span class='kn'>import</span> <span class='n'>Number</span><span class='p'>,</span> <span class='n'>identity</span>
+
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[</span><span class='s2'>&quot;SignalGenerator&quot;</span><span class='p'>,</span> <span class='s2'>&quot;multifreq&quot;</span><span class='p'>,</span> <span class='s2'>&quot;triangle&quot;</span><span class='p'>,</span> <span class='s2'>&quot;sawtooth&quot;</span><span class='p'>]</span>
+
+
+<div class='viewcode-block' id='multifreq'><a class='viewcode-back' href='../../../generated/modulation.data.signal_generator.multifreq.html#modulation.data.signal_generator.multifreq'>[docs]</a><span class='k'>def</span> <span class='nf'>multifreq</span><span class='p'>(</span><span class='n'>x</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='k'>return</span> <span class='p'>(</span>
+        <span class='mi'>2</span>
+        <span class='o'>+</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sin</span><span class='p'>(</span><span class='n'>x</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>pi</span><span class='p'>)</span>
+        <span class='o'>+</span> <span class='mf'>0.5</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sin</span><span class='p'>(</span><span class='mi'>2</span> <span class='o'>*</span> <span class='n'>x</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>pi</span><span class='p'>)</span>
+        <span class='o'>-</span> <span class='mf'>0.2</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>cos</span><span class='p'>(</span><span class='mi'>5</span> <span class='o'>*</span> <span class='n'>x</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>pi</span><span class='p'>)</span>
+    <span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='triangle'><a class='viewcode-back' href='../../../generated/modulation.data.signal_generator.triangle.html#modulation.data.signal_generator.triangle'>[docs]</a><span class='k'>def</span> <span class='nf'>triangle</span><span class='p'>(</span><span class='n'>x</span><span class='p'>,</span> <span class='n'>section_length</span><span class='o'>=</span><span class='mf'>0.5</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+
+    <span class='n'>section0</span> <span class='o'>=</span> <span class='n'>x</span> <span class='o'>&lt;</span> <span class='n'>section_length</span>
+    <span class='n'>section1</span> <span class='o'>=</span> <span class='p'>(</span><span class='n'>x</span> <span class='o'>&gt;=</span> <span class='n'>section_length</span><span class='p'>)</span> <span class='o'>&amp;</span> <span class='p'>(</span><span class='n'>x</span> <span class='o'>&lt;</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>section_length</span><span class='p'>)</span>
+    <span class='n'>section2</span> <span class='o'>=</span> <span class='p'>(</span><span class='n'>x</span> <span class='o'>&gt;=</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>section_length</span><span class='p'>)</span> <span class='o'>&amp;</span> <span class='p'>(</span><span class='n'>x</span> <span class='o'>&lt;</span> <span class='mi'>3</span> <span class='o'>*</span> <span class='n'>section_length</span><span class='p'>)</span>
+    <span class='n'>section3</span> <span class='o'>=</span> <span class='n'>x</span> <span class='o'>&gt;=</span> <span class='mi'>3</span> <span class='o'>*</span> <span class='n'>section_length</span>
+    <span class='n'>output</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>zeros_like</span><span class='p'>(</span><span class='n'>x</span><span class='p'>)</span>
+    <span class='n'>output</span><span class='p'>[</span><span class='n'>section0</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>x</span><span class='p'>[</span><span class='n'>section0</span><span class='p'>]</span>
+    <span class='n'>output</span><span class='p'>[</span><span class='n'>section1</span><span class='p'>]</span> <span class='o'>=</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>section_length</span> <span class='o'>-</span> <span class='n'>x</span><span class='p'>[</span><span class='n'>section1</span><span class='p'>]</span>
+    <span class='n'>output</span><span class='p'>[</span><span class='n'>section2</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>x</span><span class='p'>[</span><span class='n'>section2</span><span class='p'>]</span> <span class='o'>-</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>section_length</span>
+    <span class='n'>output</span><span class='p'>[</span><span class='n'>section3</span><span class='p'>]</span> <span class='o'>=</span> <span class='mi'>4</span> <span class='o'>*</span> <span class='n'>section_length</span> <span class='o'>-</span> <span class='n'>x</span><span class='p'>[</span><span class='n'>section3</span><span class='p'>]</span>
+    <span class='k'>return</span> <span class='n'>output</span></div>
+
+
+<div class='viewcode-block' id='sawtooth'><a class='viewcode-back' href='../../../generated/modulation.data.signal_generator.sawtooth.html#modulation.data.signal_generator.sawtooth'>[docs]</a><span class='k'>def</span> <span class='nf'>sawtooth</span><span class='p'>(</span><span class='n'>x</span><span class='p'>,</span> <span class='n'>section_length</span><span class='o'>=</span><span class='mf'>0.5</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+
+    <span class='n'>section0</span> <span class='o'>=</span> <span class='n'>x</span> <span class='o'>&lt;</span> <span class='n'>section_length</span>
+    <span class='n'>section1</span> <span class='o'>=</span> <span class='p'>(</span><span class='n'>x</span> <span class='o'>&gt;=</span> <span class='n'>section_length</span><span class='p'>)</span> <span class='o'>&amp;</span> <span class='p'>(</span><span class='n'>x</span> <span class='o'>&lt;</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>section_length</span><span class='p'>)</span>
+    <span class='n'>section2</span> <span class='o'>=</span> <span class='p'>(</span><span class='n'>x</span> <span class='o'>&gt;=</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>section_length</span><span class='p'>)</span> <span class='o'>&amp;</span> <span class='p'>(</span><span class='n'>x</span> <span class='o'>&lt;</span> <span class='mi'>3</span> <span class='o'>*</span> <span class='n'>section_length</span><span class='p'>)</span>
+    <span class='n'>section3</span> <span class='o'>=</span> <span class='n'>x</span> <span class='o'>&gt;=</span> <span class='mi'>3</span> <span class='o'>*</span> <span class='n'>section_length</span>
+    <span class='n'>output</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>zeros_like</span><span class='p'>(</span><span class='n'>x</span><span class='p'>)</span>
+    <span class='n'>output</span><span class='p'>[</span><span class='n'>section0</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>x</span><span class='p'>[</span><span class='n'>section0</span><span class='p'>]</span>
+    <span class='n'>output</span><span class='p'>[</span><span class='n'>section1</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>x</span><span class='p'>[</span><span class='n'>section1</span><span class='p'>]</span> <span class='o'>-</span> <span class='n'>section_length</span>
+    <span class='n'>output</span><span class='p'>[</span><span class='n'>section2</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>x</span><span class='p'>[</span><span class='n'>section2</span><span class='p'>]</span> <span class='o'>-</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>section_length</span>
+    <span class='n'>output</span><span class='p'>[</span><span class='n'>section3</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>x</span><span class='p'>[</span><span class='n'>section3</span><span class='p'>]</span> <span class='o'>-</span> <span class='mi'>3</span> <span class='o'>*</span> <span class='n'>section_length</span>
+    <span class='k'>return</span> <span class='n'>output</span></div>
 
-          </div>
-          
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-            <p class="logo"><a href="../../../index.html">
-              <img class="logo" src="../../../_static/mod.svg" alt="Logo"/>
-            </a></p>
-<h1 class="logo"><a href="../../../index.html">modulation</a></h1>
-
-
-
-
-
-
-
-
-<h3>Navigation</h3>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../generated/modulation.html">modulation</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notes</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../getting_started.html">Getting Started</a></li>
-</ul>
-
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../../../index.html">Documentation overview</a><ul>
-  <li><a href="../../index.html">Module code</a><ul>
-  <li><a href="../../modulation.html">modulation</a><ul>
-  </ul></li>
-  </ul></li>
-  </ul></li>
-</ul>
-</div>
-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../../../search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
-      <input type="submit" value="Go" />
-    </form>
-    </div>
-</div>
-<script>document.getElementById('searchbox').style.display = "block"</script>
 
+<div class='viewcode-block' id='SignalGenerator'><a class='viewcode-back' href='../../../generated/modulation.data.signal_generator.SignalGenerator.html#modulation.data.signal_generator.SignalGenerator'>[docs]</a><span class='k'>class</span> <span class='nc'>SignalGenerator</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
 
+<div class='viewcode-block' id='SignalGenerator.__init__'><a class='viewcode-back' href='../../../generated/modulation.data.signal_generator.SignalGenerator.html#modulation.data.signal_generator.SignalGenerator.__init__'>[docs]</a>    <span class='k'>def</span> <span class='fm'>__init__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='o'>*</span><span class='n'>funcs</span><span class='p'>:</span> <span class='n'>Union</span><span class='p'>[</span><span class='n'>Callable</span><span class='p'>,</span> <span class='n'>Number</span><span class='p'>],</span> <span class='n'>delta_time</span><span class='p'>:</span> <span class='nb'>float</span> <span class='o'>=</span> <span class='mf'>1.0</span><span class='p'>):</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>reset_internal_time</span><span class='p'>()</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>delta_time</span> <span class='o'>=</span> <span class='n'>delta_time</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>funcs</span> <span class='o'>=</span> <span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='o'>*</span><span class='n'>funcs</span><span class='p'>)</span></div>
 
+    <span class='k'>def</span> <span class='fm'>__iter__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>):</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>reset_internal_time</span><span class='p'>()</span>
+        <span class='k'>return</span> <span class='bp'>self</span>
 
+    <span class='k'>def</span> <span class='fm'>__next__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>):</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>t</span> <span class='o'>+=</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>delta_time</span>
+        <span class='k'>return</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>apply</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>t</span><span class='p'>)</span>
 
+<div class='viewcode-block' id='SignalGenerator.apply'><a class='viewcode-back' href='../../../generated/modulation.data.signal_generator.SignalGenerator.html#modulation.data.signal_generator.SignalGenerator.apply'>[docs]</a>    <span class='k'>def</span> <span class='nf'>apply</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>t</span><span class='p'>:</span> <span class='nb'>float</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='nb'>float</span><span class='p'>:</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
 
+<span class='sd'>        :param t:</span>
+<span class='sd'>        :type t:</span>
+<span class='sd'>        :return:</span>
+<span class='sd'>        :rtype:</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='k'>return</span> <span class='n'>reduce</span><span class='p'>(</span><span class='k'>lambda</span> <span class='n'>x</span><span class='p'>,</span> <span class='n'>y</span><span class='p'>:</span> <span class='n'>x</span> <span class='o'>+</span> <span class='n'>y</span><span class='p'>(</span><span class='n'>t</span><span class='p'>),</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>funcs</span><span class='p'>)</span></div>
 
+    <span class='k'>def</span> <span class='fm'>__call__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>t</span><span class='p'>:</span> <span class='n'>Iterable</span><span class='p'>[</span><span class='n'>Number</span><span class='p'>])</span> <span class='o'>-&gt;</span> <span class='n'>Sequence</span><span class='p'>:</span>
+        <span class='k'>return</span> <span class='p'>[</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>apply</span><span class='p'>(</span><span class='n'>i</span><span class='p'>)</span> <span class='k'>for</span> <span class='n'>i</span> <span class='ow'>in</span> <span class='n'>t</span><span class='p'>]</span>
+
+<div class='viewcode-block' id='SignalGenerator.reset_internal_time'><a class='viewcode-back' href='../../../generated/modulation.data.signal_generator.SignalGenerator.html#modulation.data.signal_generator.SignalGenerator.reset_internal_time'>[docs]</a>    <span class='k'>def</span> <span class='nf'>reset_internal_time</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>):</span>
+        <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>t</span> <span class='o'>=</span> <span class='mf'>0.0</span></div>
+
+<div class='viewcode-block' id='SignalGenerator.set_internal_time'><a class='viewcode-back' href='../../../generated/modulation.data.signal_generator.SignalGenerator.html#modulation.data.signal_generator.SignalGenerator.set_internal_time'>[docs]</a>    <span class='k'>def</span> <span class='nf'>set_internal_time</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>t</span><span class='p'>):</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>        :param t:</span>
+<span class='sd'>        :type t:</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>t</span> <span class='o'>=</span> <span class='n'>t</span></div>
+
+    <span class='k'>def</span> <span class='fm'>__enter__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>):</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>reset_internal_time</span><span class='p'>()</span>
+        <span class='k'>return</span> <span class='kc'>True</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+
+    <span class='k'>def</span> <span class='nf'>asidjashdya</span><span class='p'>():</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>        counts</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='k'>for</span> <span class='n'>_</span><span class='p'>,</span> <span class='n'>i</span> <span class='ow'>in</span> <span class='nb'>zip</span><span class='p'>(</span><span class='nb'>range</span><span class='p'>(</span><span class='mi'>10</span><span class='p'>),</span> <span class='n'>SignalGenerator</span><span class='p'>(</span><span class='n'>identity</span><span class='p'>)):</span>
+            <span class='nb'>print</span><span class='p'>(</span><span class='n'>i</span><span class='p'>)</span>
+
+    <span class='n'>asidjashdya</span><span class='p'>()</span>
+</pre>
+            </div>
+
+          </div>
 
         </div>
       </div>
-      <div class="clearer"></div>
+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <p class='logo'><a href='../../../index.html'>
+            <img alt='Logo' class='logo' src='../../../_static/mod.svg'/>
+          </a></p>
+          <h1 class='logo'><a href='../../../index.html'>modulation</a></h1>
+
+
+          <h3>Navigation</h3>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../generated/modulation.html'>modulation</a></li>
+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../../../index.html'>Documentation overview</a>
+                <ul>
+                  <li><a href='../../index.html'>Module code</a>
+                    <ul>
+                      <li><a href='../../modulation.html'>modulation</a>
+                        <ul>
+                        </ul>
+                      </li>
+                    </ul>
+                  </li>
+                </ul>
+              </li>
+            </ul>
+          </div>
+          <div id='searchbox' role='search' style='display: none'>
+            <h3 id='searchlabel'>Quick search</h3>
+            <div class='searchformwrapper'>
+              <form action='../../../search.html' class='search' method='get'>
+                <input aria-labelledby='searchlabel' autocapitalize='off' autocomplete='off' autocorrect='off' name='q' spellcheck='false' type='text'/>
+                <input type='submit' value='Go'/>
+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
+
+
+        </div>
+      </div>
+      <div class='clearer'></div>
     </div>
-    <div class="footer">
+    <div class='footer'>
       &copy;.
-      
+
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
-      
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
+
     </div>
 
-    
 
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/docs/github/html/_modules/modulation/entry_points/cli.html b/docs/github/html/_modules/modulation/entry_points/cli.html
index 1bb634d..24b24e8 100644
--- a/docs/github/html/_modules/modulation/entry_points/cli.html
+++ b/docs/github/html/_modules/modulation/entry_points/cli.html
@@ -1,183 +1,177 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
     <title>modulation.entry_points.cli &#8212; modulation 0.0.1 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/graphviz.css" />
-    <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
-    <script src="../../../_static/jquery.js"></script>
-    <script src="../../../_static/underscore.js"></script>
-    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
-    <script src="../../../_static/doctools.js"></script>
-    <link rel="canonical" href="Aivclab.github.io/modulation/_modules/modulation/entry_points/cli.html" />
-    <link rel="index" title="Index" href="../../../genindex.html" />
-    <link rel="search" title="Search" href="../../../search.html" />
-   
-  <link rel="stylesheet" href="../../../_static/custom.css" type="text/css" />
-  
-  
-  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
-
-  </head><body>
-  
-
-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          
-
-          <div class="body" role="main">
-            
-  <h1>Source code for modulation.entry_points.cli</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 09/02/2020</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">fire</span>
-<span class="kn">import</span> <span class="nn">warg</span>
-<span class="kn">from</span> <span class="nn">pyfiglet</span> <span class="kn">import</span> <span class="n">Figlet</span>
-
-<span class="kn">from</span> <span class="nn">modulation</span> <span class="kn">import</span> <span class="n">__project__</span><span class="p">,</span> <span class="n">get_version</span>
-
-<span class="n">sponsors</span> <span class="o">=</span> <span class="s2">&quot;Alexandra Institute&quot;</span>
-<span class="n">margin_percentage</span> <span class="o">=</span> <span class="mi">0</span> <span class="o">/</span> <span class="mi">6</span>
-<span class="n">terminal_width</span> <span class="o">=</span> <span class="n">warg</span><span class="o">.</span><span class="n">get_terminal_size</span><span class="p">()</span><span class="o">.</span><span class="n">columns</span>
-<span class="n">margin</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">margin_percentage</span> <span class="o">*</span> <span class="n">terminal_width</span><span class="p">)</span>
-<span class="n">width</span> <span class="o">=</span> <span class="n">terminal_width</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">margin</span>
-<span class="n">underline</span> <span class="o">=</span> <span class="s2">&quot;_&quot;</span> <span class="o">*</span> <span class="n">width</span>
-<span class="n">indent</span> <span class="o">=</span> <span class="s2">&quot; &quot;</span> <span class="o">*</span> <span class="n">margin</span>
-
-
-<div class="viewcode-block" id="ModulationCLI"><a class="viewcode-back" href="../../../generated/modulation.entry_points.cli.ModulationCLI.html#modulation.entry_points.cli.ModulationCLI">[docs]</a><span class="k">class</span> <span class="nc">ModulationCLI</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot; &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="ModulationCLI.run"><a class="viewcode-back" href="../../../generated/modulation.entry_points.cli.ModulationCLI.html#modulation.entry_points.cli.ModulationCLI.run">[docs]</a>    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-        <span class="k">pass</span></div>
-
-<div class="viewcode-block" id="ModulationCLI.version"><a class="viewcode-back" href="../../../generated/modulation.entry_points.cli.ModulationCLI.html#modulation.entry_points.cli.ModulationCLI.version">[docs]</a>    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">version</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Prints the version of this installation.&quot;&quot;&quot;</span>
-        <span class="n">draw_cli_header</span><span class="p">()</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Version: </span><span class="si">{</span><span class="n">get_version</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="ModulationCLI.sponsors"><a class="viewcode-back" href="../../../generated/modulation.entry_points.cli.ModulationCLI.html#modulation.entry_points.cli.ModulationCLI.sponsors">[docs]</a>    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">sponsors</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">sponsors</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="draw_cli_header"><a class="viewcode-back" href="../../../generated/modulation.entry_points.cli.draw_cli_header.html#modulation.entry_points.cli.draw_cli_header">[docs]</a><span class="k">def</span> <span class="nf">draw_cli_header</span><span class="p">(</span><span class="o">*</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">__project__</span><span class="p">,</span> <span class="n">font</span><span class="o">=</span><span class="s2">&quot;big&quot;</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param title:</span>
-<span class="sd">    :type title:</span>
-<span class="sd">    :param font:</span>
-<span class="sd">    :type font:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">figlet</span> <span class="o">=</span> <span class="n">Figlet</span><span class="p">(</span><span class="n">font</span><span class="o">=</span><span class="n">font</span><span class="p">,</span> <span class="n">justify</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="n">terminal_width</span><span class="p">)</span>
-    <span class="n">description</span> <span class="o">=</span> <span class="n">figlet</span><span class="o">.</span><span class="n">renderText</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">description</span><span class="si">}{</span><span class="n">underline</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="main"><a class="viewcode-back" href="../../../generated/modulation.entry_points.cli.main.html#modulation.entry_points.cli.main">[docs]</a><span class="k">def</span> <span class="nf">main</span><span class="p">(</span><span class="o">*</span><span class="p">,</span> <span class="n">always_draw_header</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param always_draw_header:</span>
-<span class="sd">    :type always_draw_header:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">always_draw_header</span><span class="p">:</span>
-        <span class="n">draw_cli_header</span><span class="p">()</span>
-    <span class="n">fire</span><span class="o">.</span><span class="n">Fire</span><span class="p">(</span><span class="n">ModulationCLI</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;modulation&quot;</span><span class="p">)</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-    <span class="n">main</span><span class="p">()</span>
-</pre></div>
+    <link href='../../../_static/pygments.css' rel='stylesheet' type='text/css'/>
+    <link href='../../../_static/alabaster.css' rel='stylesheet' type='text/css'/>
+    <link href='../../../_static/graphviz.css' rel='stylesheet' type='text/css'/>
+    <script data-url_root='../../../' id='documentation_options' src='../../../_static/documentation_options.js'></script>
+    <script src='../../../_static/jquery.js'></script>
+    <script src='../../../_static/underscore.js'></script>
+    <script src='../../../_static/_sphinx_javascript_frameworks_compat.js'></script>
+    <script src='../../../_static/doctools.js'></script>
+    <link href='Aivclab.github.io/modulation/_modules/modulation/entry_points/cli.html' rel='canonical'/>
+    <link href='../../../genindex.html' rel='index' title='Index'/>
+    <link href='../../../search.html' rel='search' title='Search'/>
 
-          </div>
-          
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-            <p class="logo"><a href="../../../index.html">
-              <img class="logo" src="../../../_static/mod.svg" alt="Logo"/>
-            </a></p>
-<h1 class="logo"><a href="../../../index.html">modulation</a></h1>
-
-
-
-
-
-
-
-
-<h3>Navigation</h3>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../generated/modulation.html">modulation</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notes</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../getting_started.html">Getting Started</a></li>
-</ul>
-
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../../../index.html">Documentation overview</a><ul>
-  <li><a href="../../index.html">Module code</a><ul>
-  <li><a href="../../modulation.html">modulation</a><ul>
-  </ul></li>
-  </ul></li>
-  </ul></li>
-</ul>
-</div>
-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../../../search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
-      <input type="submit" value="Go" />
-    </form>
-    </div>
-</div>
-<script>document.getElementById('searchbox').style.display = "block"</script>
+    <link href='../../../_static/custom.css' rel='stylesheet' type='text/css'/>
+
+
+    <meta content='width=device-width, initial-scale=0.9, maximum-scale=0.9' name='viewport'/>
+
+  </head>
+  <body>
+
+
+    <div class='document'>
+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
+
+
+          <div class='body' role='main'>
+
+            <h1>Source code for modulation.entry_points.cli</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 09/02/2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>import</span> <span class='nn'>fire</span>
+<span class='kn'>import</span> <span class='nn'>warg</span>
+<span class='kn'>from</span> <span class='nn'>pyfiglet</span> <span class='kn'>import</span> <span class='n'>Figlet</span>
+
+<span class='kn'>from</span> <span class='nn'>modulation</span> <span class='kn'>import</span> <span class='n'>__project__</span><span class='p'>,</span> <span class='n'>get_version</span>
+
+<span class='n'>sponsors</span> <span class='o'>=</span> <span class='s2'>&quot;Alexandra Institute&quot;</span>
+<span class='n'>margin_percentage</span> <span class='o'>=</span> <span class='mi'>0</span> <span class='o'>/</span> <span class='mi'>6</span>
+<span class='n'>terminal_width</span> <span class='o'>=</span> <span class='n'>warg</span><span class='o'>.</span><span class='n'>get_terminal_size</span><span class='p'>()</span><span class='o'>.</span><span class='n'>columns</span>
+<span class='n'>margin</span> <span class='o'>=</span> <span class='nb'>int</span><span class='p'>(</span><span class='n'>margin_percentage</span> <span class='o'>*</span> <span class='n'>terminal_width</span><span class='p'>)</span>
+<span class='n'>width</span> <span class='o'>=</span> <span class='n'>terminal_width</span> <span class='o'>-</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>margin</span>
+<span class='n'>underline</span> <span class='o'>=</span> <span class='s2'>&quot;_&quot;</span> <span class='o'>*</span> <span class='n'>width</span>
+<span class='n'>indent</span> <span class='o'>=</span> <span class='s2'>&quot; &quot;</span> <span class='o'>*</span> <span class='n'>margin</span>
 
 
+<div class='viewcode-block' id='ModulationCLI'><a class='viewcode-back' href='../../../generated/modulation.entry_points.cli.ModulationCLI.html#modulation.entry_points.cli.ModulationCLI'>[docs]</a><span class='k'>class</span> <span class='nc'>ModulationCLI</span><span class='p'>(</span><span class='nb'>object</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot; &quot;&quot;&quot;</span>
 
+<div class='viewcode-block' id='ModulationCLI.run'><a class='viewcode-back' href='../../../generated/modulation.entry_points.cli.ModulationCLI.html#modulation.entry_points.cli.ModulationCLI.run'>[docs]</a>    <span class='k'>def</span> <span class='nf'>run</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='kc'>None</span><span class='p'>:</span>
+        <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+        <span class='k'>pass</span></div>
 
+<div class='viewcode-block' id='ModulationCLI.version'><a class='viewcode-back' href='../../../generated/modulation.entry_points.cli.ModulationCLI.html#modulation.entry_points.cli.ModulationCLI.version'>[docs]</a>    <span class='nd'>@staticmethod</span>
+    <span class='k'>def</span> <span class='nf'>version</span><span class='p'>()</span> <span class='o'>-&gt;</span> <span class='kc'>None</span><span class='p'>:</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>        Prints the version of this installation.&quot;&quot;&quot;</span>
+        <span class='n'>draw_cli_header</span><span class='p'>()</span>
+        <span class='nb'>print</span><span class='p'>(</span><span class='sa'>f</span><span class='s2'>&quot;Version: </span><span class='si'>{</span><span class='n'>get_version</span><span class='p'>()</span><span class='si'>}</span><span class='s2'>&quot;</span><span class='p'>)</span></div>
 
+<div class='viewcode-block' id='ModulationCLI.sponsors'><a class='viewcode-back' href='../../../generated/modulation.entry_points.cli.ModulationCLI.html#modulation.entry_points.cli.ModulationCLI.sponsors'>[docs]</a>    <span class='nd'>@staticmethod</span>
+    <span class='k'>def</span> <span class='nf'>sponsors</span><span class='p'>()</span> <span class='o'>-&gt;</span> <span class='kc'>None</span><span class='p'>:</span>
+        <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+        <span class='nb'>print</span><span class='p'>(</span><span class='n'>sponsors</span><span class='p'>)</span></div></div>
 
 
+<div class='viewcode-block' id='draw_cli_header'><a class='viewcode-back' href='../../../generated/modulation.entry_points.cli.draw_cli_header.html#modulation.entry_points.cli.draw_cli_header'>[docs]</a><span class='k'>def</span> <span class='nf'>draw_cli_header</span><span class='p'>(</span><span class='o'>*</span><span class='p'>,</span> <span class='n'>title</span><span class='o'>=</span><span class='n'>__project__</span><span class='p'>,</span> <span class='n'>font</span><span class='o'>=</span><span class='s2'>&quot;big&quot;</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param title:</span>
+<span class='sd'>    :type title:</span>
+<span class='sd'>    :param font:</span>
+<span class='sd'>    :type font:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>figlet</span> <span class='o'>=</span> <span class='n'>Figlet</span><span class='p'>(</span><span class='n'>font</span><span class='o'>=</span><span class='n'>font</span><span class='p'>,</span> <span class='n'>justify</span><span class='o'>=</span><span class='s2'>&quot;center&quot;</span><span class='p'>,</span> <span class='n'>width</span><span class='o'>=</span><span class='n'>terminal_width</span><span class='p'>)</span>
+    <span class='n'>description</span> <span class='o'>=</span> <span class='n'>figlet</span><span class='o'>.</span><span class='n'>renderText</span><span class='p'>(</span><span class='n'>title</span><span class='p'>)</span>
+
+    <span class='nb'>print</span><span class='p'>(</span><span class='sa'>f</span><span class='s2'>&quot;</span><span class='si'>{</span><span class='n'>description</span><span class='si'>}{</span><span class='n'>underline</span><span class='si'>}</span><span class='se'>\n</span><span class='s2'>&quot;</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='main'><a class='viewcode-back' href='../../../generated/modulation.entry_points.cli.main.html#modulation.entry_points.cli.main'>[docs]</a><span class='k'>def</span> <span class='nf'>main</span><span class='p'>(</span><span class='o'>*</span><span class='p'>,</span> <span class='n'>always_draw_header</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param always_draw_header:</span>
+<span class='sd'>    :type always_draw_header:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>if</span> <span class='n'>always_draw_header</span><span class='p'>:</span>
+        <span class='n'>draw_cli_header</span><span class='p'>()</span>
+    <span class='n'>fire</span><span class='o'>.</span><span class='n'>Fire</span><span class='p'>(</span><span class='n'>ModulationCLI</span><span class='p'>,</span> <span class='n'>name</span><span class='o'>=</span><span class='s2'>&quot;modulation&quot;</span><span class='p'>)</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+    <span class='n'>main</span><span class='p'>()</span>
+</pre>
+            </div>
+
+          </div>
 
         </div>
       </div>
-      <div class="clearer"></div>
+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <p class='logo'><a href='../../../index.html'>
+            <img alt='Logo' class='logo' src='../../../_static/mod.svg'/>
+          </a></p>
+          <h1 class='logo'><a href='../../../index.html'>modulation</a></h1>
+
+
+          <h3>Navigation</h3>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../generated/modulation.html'>modulation</a></li>
+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../../../index.html'>Documentation overview</a>
+                <ul>
+                  <li><a href='../../index.html'>Module code</a>
+                    <ul>
+                      <li><a href='../../modulation.html'>modulation</a>
+                        <ul>
+                        </ul>
+                      </li>
+                    </ul>
+                  </li>
+                </ul>
+              </li>
+            </ul>
+          </div>
+          <div id='searchbox' role='search' style='display: none'>
+            <h3 id='searchlabel'>Quick search</h3>
+            <div class='searchformwrapper'>
+              <form action='../../../search.html' class='search' method='get'>
+                <input aria-labelledby='searchlabel' autocapitalize='off' autocomplete='off' autocorrect='off' name='q' spellcheck='false' type='text'/>
+                <input type='submit' value='Go'/>
+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
+
+
+        </div>
+      </div>
+      <div class='clearer'></div>
     </div>
-    <div class="footer">
+    <div class='footer'>
       &copy;.
-      
+
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
-      
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
+
     </div>
 
-    
 
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/docs/github/html/_modules/modulation/entry_points/tensorboard_entry_point.html b/docs/github/html/_modules/modulation/entry_points/tensorboard_entry_point.html
index 9c5522a..306b0ec 100644
--- a/docs/github/html/_modules/modulation/entry_points/tensorboard_entry_point.html
+++ b/docs/github/html/_modules/modulation/entry_points/tensorboard_entry_point.html
@@ -1,171 +1,165 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
     <title>modulation.entry_points.tensorboard_entry_point &#8212; modulation 0.0.1 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/graphviz.css" />
-    <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
-    <script src="../../../_static/jquery.js"></script>
-    <script src="../../../_static/underscore.js"></script>
-    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
-    <script src="../../../_static/doctools.js"></script>
-    <link rel="canonical" href="Aivclab.github.io/modulation/_modules/modulation/entry_points/tensorboard_entry_point.html" />
-    <link rel="index" title="Index" href="../../../genindex.html" />
-    <link rel="search" title="Search" href="../../../search.html" />
-   
-  <link rel="stylesheet" href="../../../_static/custom.css" type="text/css" />
-  
-  
-  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
-
-  </head><body>
-  
-
-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          
-
-          <div class="body" role="main">
-            
-  <h1>Source code for modulation.entry_points.tensorboard_entry_point</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-<span class="kn">from</span> <span class="nn">shutil</span> <span class="kn">import</span> <span class="n">rmtree</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot; description &quot;&quot;&quot;</span>
-
-<span class="kn">from</span> <span class="nn">warg</span> <span class="kn">import</span> <span class="n">IgnoreInterruptSignal</span>
-
-
-<div class="viewcode-block" id="main"><a class="viewcode-back" href="../../../generated/modulation.entry_points.tensorboard_entry_point.main.html#modulation.entry_points.tensorboard_entry_point.main">[docs]</a><span class="k">def</span> <span class="nf">main</span><span class="p">(</span><span class="n">keep_alive</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param keep_alive:</span>
-<span class="sd">    :type keep_alive:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="kn">from</span> <span class="nn">draugr.torch_utilities</span> <span class="kn">import</span> <span class="n">launch_tensorboard</span>
-    <span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">sleep</span>
-
-    <span class="kn">from</span> <span class="nn">modulation</span> <span class="kn">import</span> <span class="n">PROJECT_APP_PATH</span>
-
-    <span class="kn">import</span> <span class="nn">argparse</span>
-
-    <span class="n">parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">&quot;Option for launching tensorboard&quot;</span><span class="p">)</span>
-    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
-        <span class="s2">&quot;--clean&quot;</span><span class="p">,</span>
-        <span class="n">action</span><span class="o">=</span><span class="s2">&quot;store_true&quot;</span><span class="p">,</span>
-        <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Adding --clean argument will wipe tensorboard logs&quot;</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
-        <span class="s2">&quot;--port&quot;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">6006</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Which port should tensorboard be served on&quot;</span>
-    <span class="p">)</span>
-    <span class="n">args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
-
-    <span class="n">log_dir</span> <span class="o">=</span> <span class="n">PROJECT_APP_PATH</span><span class="o">.</span><span class="n">user_log</span>
-
-    <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">clean</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Wiping </span><span class="si">{</span><span class="n">PROJECT_APP_PATH</span><span class="o">.</span><span class="n">user_log</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">PROJECT_APP_PATH</span><span class="o">.</span><span class="n">user_log</span><span class="o">.</span><span class="n">exists</span><span class="p">():</span>
-            <span class="n">rmtree</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">log_dir</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">PROJECT_APP_PATH</span><span class="o">.</span><span class="n">user_log</span><span class="o">.</span><span class="n">mkdir</span><span class="p">()</span>
-
-    <span class="n">address</span> <span class="o">=</span> <span class="n">launch_tensorboard</span><span class="p">(</span><span class="n">log_dir</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">port</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">keep_alive</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;tensorboard address: </span><span class="si">{</span><span class="n">address</span><span class="si">}</span><span class="s2"> for log_dir </span><span class="si">{</span><span class="n">log_dir</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-        <span class="k">with</span> <span class="n">IgnoreInterruptSignal</span><span class="p">():</span>
-            <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
-                <span class="n">sleep</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">address</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-    <span class="n">main</span><span class="p">()</span>
-</pre></div>
+    <link href='../../../_static/pygments.css' rel='stylesheet' type='text/css'/>
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+    <link href='../../../search.html' rel='search' title='Search'/>
 
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-<h3>Related Topics</h3>
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+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
+
+
+          <div class='body' role='main'>
+
+            <h1>Source code for modulation.entry_points.tensorboard_entry_point</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+<span class='kn'>from</span> <span class='nn'>shutil</span> <span class='kn'>import</span> <span class='n'>rmtree</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='s2'>&quot;&quot;&quot; description &quot;&quot;&quot;</span>
+
+<span class='kn'>from</span> <span class='nn'>warg</span> <span class='kn'>import</span> <span class='n'>IgnoreInterruptSignal</span>
 
 
+<div class='viewcode-block' id='main'><a class='viewcode-back' href='../../../generated/modulation.entry_points.tensorboard_entry_point.main.html#modulation.entry_points.tensorboard_entry_point.main'>[docs]</a><span class='k'>def</span> <span class='nf'>main</span><span class='p'>(</span><span class='n'>keep_alive</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
 
+<span class='sd'>    :param keep_alive:</span>
+<span class='sd'>    :type keep_alive:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='kn'>from</span> <span class='nn'>draugr.torch_utilities</span> <span class='kn'>import</span> <span class='n'>launch_tensorboard</span>
+    <span class='kn'>from</span> <span class='nn'>time</span> <span class='kn'>import</span> <span class='n'>sleep</span>
 
+    <span class='kn'>from</span> <span class='nn'>modulation</span> <span class='kn'>import</span> <span class='n'>PROJECT_APP_PATH</span>
 
+    <span class='kn'>import</span> <span class='nn'>argparse</span>
 
+    <span class='n'>parser</span> <span class='o'>=</span> <span class='n'>argparse</span><span class='o'>.</span><span class='n'>ArgumentParser</span><span class='p'>(</span><span class='n'>description</span><span class='o'>=</span><span class='s2'>&quot;Option for launching tensorboard&quot;</span><span class='p'>)</span>
+    <span class='n'>parser</span><span class='o'>.</span><span class='n'>add_argument</span><span class='p'>(</span>
+        <span class='s2'>&quot;--clean&quot;</span><span class='p'>,</span>
+        <span class='n'>action</span><span class='o'>=</span><span class='s2'>&quot;store_true&quot;</span><span class='p'>,</span>
+        <span class='n'>default</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>,</span>
+        <span class='n'>help</span><span class='o'>=</span><span class='s2'>&quot;Adding --clean argument will wipe tensorboard logs&quot;</span><span class='p'>,</span>
+    <span class='p'>)</span>
+    <span class='n'>parser</span><span class='o'>.</span><span class='n'>add_argument</span><span class='p'>(</span>
+        <span class='s2'>&quot;--port&quot;</span><span class='p'>,</span> <span class='n'>default</span><span class='o'>=</span><span class='mi'>6006</span><span class='p'>,</span> <span class='n'>help</span><span class='o'>=</span><span class='s2'>&quot;Which port should tensorboard be served on&quot;</span>
+    <span class='p'>)</span>
+    <span class='n'>args</span> <span class='o'>=</span> <span class='n'>parser</span><span class='o'>.</span><span class='n'>parse_args</span><span class='p'>()</span>
 
+    <span class='n'>log_dir</span> <span class='o'>=</span> <span class='n'>PROJECT_APP_PATH</span><span class='o'>.</span><span class='n'>user_log</span>
+
+    <span class='k'>if</span> <span class='n'>args</span><span class='o'>.</span><span class='n'>clean</span><span class='p'>:</span>
+        <span class='nb'>print</span><span class='p'>(</span><span class='sa'>f</span><span class='s2'>&quot;Wiping </span><span class='si'>{</span><span class='n'>PROJECT_APP_PATH</span><span class='o'>.</span><span class='n'>user_log</span><span class='si'>}</span><span class='s2'>&quot;</span><span class='p'>)</span>
+        <span class='k'>if</span> <span class='n'>PROJECT_APP_PATH</span><span class='o'>.</span><span class='n'>user_log</span><span class='o'>.</span><span class='n'>exists</span><span class='p'>():</span>
+            <span class='n'>rmtree</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='n'>log_dir</span><span class='p'>))</span>
+        <span class='k'>else</span><span class='p'>:</span>
+            <span class='n'>PROJECT_APP_PATH</span><span class='o'>.</span><span class='n'>user_log</span><span class='o'>.</span><span class='n'>mkdir</span><span class='p'>()</span>
+
+    <span class='n'>address</span> <span class='o'>=</span> <span class='n'>launch_tensorboard</span><span class='p'>(</span><span class='n'>log_dir</span><span class='p'>,</span> <span class='n'>args</span><span class='o'>.</span><span class='n'>port</span><span class='p'>)</span>
+
+    <span class='k'>if</span> <span class='n'>keep_alive</span><span class='p'>:</span>
+        <span class='nb'>print</span><span class='p'>(</span><span class='sa'>f</span><span class='s2'>&quot;tensorboard address: </span><span class='si'>{</span><span class='n'>address</span><span class='si'>}</span><span class='s2'> for log_dir </span><span class='si'>{</span><span class='n'>log_dir</span><span class='si'>}</span><span class='s2'>&quot;</span><span class='p'>)</span>
+        <span class='k'>with</span> <span class='n'>IgnoreInterruptSignal</span><span class='p'>():</span>
+            <span class='k'>while</span> <span class='kc'>True</span><span class='p'>:</span>
+                <span class='n'>sleep</span><span class='p'>(</span><span class='mi'>100</span><span class='p'>)</span>
+    <span class='k'>else</span><span class='p'>:</span>
+        <span class='k'>return</span> <span class='n'>address</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+    <span class='n'>main</span><span class='p'>()</span>
+</pre>
+            </div>
+
+          </div>
 
         </div>
       </div>
-      <div class="clearer"></div>
+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <p class='logo'><a href='../../../index.html'>
+            <img alt='Logo' class='logo' src='../../../_static/mod.svg'/>
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../generated/modulation.html'>modulation</a></li>
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+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../../../index.html'>Documentation overview</a>
+                <ul>
+                  <li><a href='../../index.html'>Module code</a>
+                    <ul>
+                      <li><a href='../../modulation.html'>modulation</a>
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+          <div id='searchbox' role='search' style='display: none'>
+            <h3 id='searchlabel'>Quick search</h3>
+            <div class='searchformwrapper'>
+              <form action='../../../search.html' class='search' method='get'>
+                <input aria-labelledby='searchlabel' autocapitalize='off' autocomplete='off' autocorrect='off' name='q' spellcheck='false' type='text'/>
+                <input type='submit' value='Go'/>
+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
+
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+      </div>
+      <div class='clearer'></div>
     </div>
-    <div class="footer">
+    <div class='footer'>
       &copy;.
-      
+
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
-      
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
+
     </div>
 
-    
 
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\ No newline at end of file
diff --git a/docs/github/html/_modules/modulation/noise_generation/additive_noise.html b/docs/github/html/_modules/modulation/noise_generation/additive_noise.html
new file mode 100644
index 0000000..a5091e5
--- /dev/null
+++ b/docs/github/html/_modules/modulation/noise_generation/additive_noise.html
@@ -0,0 +1,217 @@
+<!DOCTYPE html>
+
+<html lang='en'>
+  <head>
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
+    <title>modulation.noise_generation.additive_noise &#8212; modulation 0.0.1 documentation</title>
+    <link href='../../../_static/pygments.css' rel='stylesheet' type='text/css'/>
+    <link href='../../../_static/alabaster.css' rel='stylesheet' type='text/css'/>
+    <link href='../../../_static/graphviz.css' rel='stylesheet' type='text/css'/>
+    <script data-url_root='../../../' id='documentation_options' src='../../../_static/documentation_options.js'></script>
+    <script src='../../../_static/jquery.js'></script>
+    <script src='../../../_static/underscore.js'></script>
+    <script src='../../../_static/_sphinx_javascript_frameworks_compat.js'></script>
+    <script src='../../../_static/doctools.js'></script>
+    <link href='aivclab.github.io/modulation/_modules/modulation/noise_generation/additive_noise.html' rel='canonical'/>
+    <link href='../../../genindex.html' rel='index' title='Index'/>
+    <link href='../../../search.html' rel='search' title='Search'/>
+
+    <link href='../../../_static/custom.css' rel='stylesheet' type='text/css'/>
+
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+
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+  <body>
+
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+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
+
+
+          <div class='body' role='main'>
+
+            <h1>Source code for modulation.noise_generation.additive_noise</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 10-12-2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>from</span> <span class='nn'>pathlib</span> <span class='kn'>import</span> <span class='n'>Path</span>
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Any</span>
+
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>from</span> <span class='nn'>apppath</span> <span class='kn'>import</span> <span class='n'>ensure_existence</span>
+<span class='kn'>from</span> <span class='nn'>draugr.numpy_utilities</span> <span class='kn'>import</span> <span class='n'>root_mean_square</span>
+<span class='kn'>from</span> <span class='nn'>draugr.tqdm_utilities</span> <span class='kn'>import</span> <span class='n'>progress_bar</span>
+<span class='kn'>from</span> <span class='nn'>scipy</span> <span class='kn'>import</span> <span class='n'>signal</span>
+<span class='kn'>from</span> <span class='nn'>scipy.io</span> <span class='kn'>import</span> <span class='n'>wavfile</span>
+
+<span class='kn'>from</span> <span class='nn'>modulation.audio_utilities.splitting</span> <span class='kn'>import</span> <span class='n'>mask_split_speech_silence</span>
+
+
+<div class='viewcode-block' id='sample_noise'><a class='viewcode-back' href='../../../generated/modulation.noise_generation.additive_noise.sample_noise.html#modulation.noise_generation.additive_noise.sample_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>sample_noise</span><span class='p'>(</span>
+    <span class='n'>noise</span><span class='p'>,</span>
+    <span class='n'>noise_rate</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span>
+    <span class='n'>signal_len</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span>
+    <span class='n'>signal_rate</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span>
+    <span class='o'>*</span><span class='p'>,</span>
+    <span class='n'>resample_noise</span><span class='p'>:</span> <span class='nb'>bool</span> <span class='o'>=</span> <span class='kc'>True</span><span class='p'>,</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Any</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param noise:</span>
+<span class='sd'>    :type noise:</span>
+<span class='sd'>    :param noise_rate:</span>
+<span class='sd'>    :type noise_rate:</span>
+<span class='sd'>    :param signal_len:</span>
+<span class='sd'>    :type signal_len:</span>
+<span class='sd'>    :param signal_rate:</span>
+<span class='sd'>    :type signal_rate:</span>
+<span class='sd'>    :param resample_noise:</span>
+<span class='sd'>    :type resample_noise:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>noise_len</span> <span class='o'>=</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>noise</span><span class='p'>)</span>
+    <span class='k'>if</span> <span class='n'>resample_noise</span><span class='p'>:</span>
+        <span class='n'>noise</span> <span class='o'>=</span> <span class='n'>signal</span><span class='o'>.</span><span class='n'>resample</span><span class='p'>(</span><span class='n'>noise</span><span class='p'>,</span> <span class='nb'>round</span><span class='p'>(</span><span class='n'>noise_len</span> <span class='o'>*</span> <span class='nb'>float</span><span class='p'>(</span><span class='n'>signal_rate</span><span class='p'>)</span> <span class='o'>/</span> <span class='n'>noise_rate</span><span class='p'>))</span>
+
+    <span class='n'>start_index</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>random</span><span class='o'>.</span><span class='n'>randint</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='n'>noise_len</span><span class='p'>)</span>
+    <span class='n'>noise</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>tile</span><span class='p'>(</span><span class='n'>noise</span><span class='p'>,</span> <span class='p'>(</span><span class='n'>signal_len</span> <span class='o'>//</span> <span class='n'>noise_len</span><span class='p'>)</span> <span class='o'>+</span> <span class='mi'>2</span><span class='p'>)</span>  <span class='c1'># at least tile once (=2)</span>
+    <span class='k'>return</span> <span class='n'>noise</span><span class='p'>[</span><span class='n'>start_index</span> <span class='p'>:</span> <span class='n'>signal_len</span> <span class='o'>+</span> <span class='n'>start_index</span><span class='p'>]</span></div>
+
+
+<div class='viewcode-block' id='compute_additive_noise_samples'><a class='viewcode-back' href='../../../generated/modulation.noise_generation.additive_noise.compute_additive_noise_samples.html#modulation.noise_generation.additive_noise.compute_additive_noise_samples'>[docs]</a><span class='k'>def</span> <span class='nf'>compute_additive_noise_samples</span><span class='p'>(</span>
+    <span class='o'>*</span><span class='p'>,</span>
+    <span class='n'>voice_activity_mask</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span>
+    <span class='n'>signal_file</span><span class='p'>:</span> <span class='n'>Path</span><span class='p'>,</span>
+    <span class='n'>category</span><span class='p'>,</span>
+    <span class='n'>out_dir</span><span class='p'>,</span>
+    <span class='n'>noise_file</span><span class='p'>,</span>
+    <span class='n'>snrs</span><span class='o'>=</span><span class='nb'>list</span><span class='p'>((</span><span class='n'>i</span> <span class='o'>*</span> <span class='mi'>5</span> <span class='k'>for</span> <span class='n'>i</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='mi'>5</span><span class='p'>))),</span>
+    <span class='n'>verbose</span><span class='p'>:</span> <span class='nb'>bool</span> <span class='o'>=</span> <span class='kc'>False</span><span class='p'>,</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='kc'>None</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param voice_activity_mask:</span>
+<span class='sd'>    :type voice_activity_mask:</span>
+<span class='sd'>    :param signal_file:</span>
+<span class='sd'>    :type signal_file:</span>
+<span class='sd'>    :param category:</span>
+<span class='sd'>    :type category:</span>
+<span class='sd'>    :param out_dir:</span>
+<span class='sd'>    :type out_dir:</span>
+<span class='sd'>    :param noise_file:</span>
+<span class='sd'>    :type noise_file:</span>
+<span class='sd'>    :param snrs:</span>
+<span class='sd'>    :type snrs:</span>
+<span class='sd'>    :param verbose:</span>
+<span class='sd'>    :type verbose:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>sr_noise</span><span class='p'>,</span> <span class='n'>noise</span> <span class='o'>=</span> <span class='n'>wavfile</span><span class='o'>.</span><span class='n'>read</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='n'>noise_file</span><span class='p'>))</span>
+    <span class='n'>sr_signal</span><span class='p'>,</span> <span class='n'>signal</span> <span class='o'>=</span> <span class='n'>wavfile</span><span class='o'>.</span><span class='n'>read</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='n'>signal_file</span><span class='p'>))</span>
+
+    <span class='n'>max_sample</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>max</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>)</span>
+    <span class='n'>signal</span> <span class='o'>=</span> <span class='n'>signal</span> <span class='o'>/</span> <span class='n'>max_sample</span>
+
+    <span class='n'>noise_part</span> <span class='o'>=</span> <span class='n'>sample_noise</span><span class='p'>(</span>
+        <span class='n'>noise</span> <span class='o'>/</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>max</span><span class='p'>(</span><span class='n'>noise</span><span class='p'>),</span>
+        <span class='n'>noise_rate</span><span class='o'>=</span><span class='n'>sr_noise</span><span class='p'>,</span>
+        <span class='n'>signal_len</span><span class='o'>=</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>),</span>
+        <span class='n'>signal_rate</span><span class='o'>=</span><span class='n'>sr_signal</span><span class='p'>,</span>
+    <span class='p'>)</span>
+
+    <span class='n'>noise_scaled</span> <span class='o'>=</span> <span class='n'>noise_part</span> <span class='o'>*</span> <span class='p'>(</span>
+        <span class='n'>root_mean_square</span><span class='p'>(</span><span class='n'>mask_split_speech_silence</span><span class='p'>(</span><span class='n'>voice_activity_mask</span><span class='p'>,</span> <span class='n'>signal</span><span class='p'>)[</span><span class='mi'>0</span><span class='p'>])</span> <span class='o'>/</span> <span class='n'>root_mean_square</span><span class='p'>(</span><span class='n'>noise_part</span><span class='p'>)</span>
+    <span class='p'>)</span>  <span class='c1'># scaled by ratio of speech to noise level</span>
+
+    <span class='k'>for</span> <span class='n'>snr</span> <span class='ow'>in</span> <span class='n'>progress_bar</span><span class='p'>(</span><span class='n'>snrs</span><span class='p'>,</span> <span class='n'>disable</span><span class='o'>=</span><span class='ow'>not</span> <span class='n'>verbose</span><span class='p'>):</span>
+        <span class='n'>noised</span> <span class='o'>=</span> <span class='n'>signal</span> <span class='o'>+</span> <span class='n'>noise_scaled</span> <span class='o'>/</span> <span class='p'>(</span><span class='mi'>10</span> <span class='o'>**</span> <span class='p'>(</span><span class='n'>snr</span> <span class='o'>/</span> <span class='mi'>20</span><span class='p'>))</span>
+        <span class='n'>dest</span> <span class='o'>=</span> <span class='nb'>str</span><span class='p'>(</span>
+            <span class='n'>ensure_existence</span><span class='p'>(</span><span class='n'>out_dir</span> <span class='o'>/</span> <span class='sa'>f</span><span class='s1'>&#39;</span><span class='si'>{</span><span class='n'>noise_file</span><span class='o'>.</span><span class='n'>with_suffix</span><span class='p'>(</span><span class='s2'>&quot;&quot;</span><span class='p'>)</span><span class='o'>.</span><span class='n'>name</span><span class='si'>}</span><span class='s1'>_SNR_</span><span class='si'>{</span><span class='n'>snr</span><span class='si'>}</span><span class='s1'>dB&#39;</span> <span class='o'>/</span> <span class='n'>category</span><span class='p'>)</span> <span class='o'>/</span> <span class='n'>signal_file</span><span class='o'>.</span><span class='n'>name</span>
+        <span class='p'>)</span>
+        <span class='k'>if</span> <span class='n'>verbose</span><span class='p'>:</span>
+            <span class='nb'>print</span><span class='p'>(</span><span class='sa'>f</span><span class='s2'>&quot;writing wavfile </span><span class='si'>{</span><span class='n'>dest</span><span class='si'>}</span><span class='s2'>&quot;</span><span class='p'>)</span>
+        <span class='n'>wavfile</span><span class='o'>.</span><span class='n'>write</span><span class='p'>(</span>
+            <span class='n'>dest</span><span class='p'>,</span>
+            <span class='n'>sr_signal</span><span class='p'>,</span>
+            <span class='p'>((</span><span class='n'>noised</span> <span class='o'>/</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>max</span><span class='p'>(</span><span class='n'>noised</span><span class='p'>))</span> <span class='o'>*</span> <span class='n'>max_sample</span><span class='p'>)</span><span class='o'>.</span><span class='n'>astype</span><span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>int16</span><span class='p'>),</span>
+        <span class='p'>)</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+    <span class='k'>pass</span>
+</pre>
+            </div>
+
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+
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../getting_started.html'>Getting Started</a></li>
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+
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+              <li><a href='../../../index.html'>Documentation overview</a>
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+<!DOCTYPE html>
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+  <head>
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+
+            <h1>Source code for modulation.noise_generation.babble_noise</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 09-12-2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>from</span> <span class='nn'>pathlib</span> <span class='kn'>import</span> <span class='n'>Path</span>
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Iterable</span><span class='p'>,</span> <span class='n'>Sequence</span>
+
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>import</span> <span class='nn'>torchaudio</span>
+<span class='kn'>from</span> <span class='nn'>draugr.numpy_utilities.signal_utilities.truncation</span> <span class='kn'>import</span> <span class='n'>min_length_truncate_batch</span>
+<span class='kn'>from</span> <span class='nn'>draugr.torch_utilities</span> <span class='kn'>import</span> <span class='n'>to_tensor</span>
+
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[</span><span class='s2'>&quot;generate_babble_noise&quot;</span><span class='p'>]</span>
+
+
+<div class='viewcode-block' id='generate_babble_noise'><a class='viewcode-back' href='../../../generated/modulation.noise_generation.babble_noise.generate_babble_noise.html#modulation.noise_generation.babble_noise.generate_babble_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>generate_babble_noise</span><span class='p'>(</span>
+    <span class='n'>samples</span><span class='p'>:</span> <span class='n'>Iterable</span><span class='p'>[</span><span class='n'>Iterable</span><span class='p'>[</span><span class='n'>Sequence</span><span class='p'>]],</span>
+    <span class='n'>sampling_rate</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span>
+    <span class='o'>*</span><span class='p'>,</span>
+    <span class='n'>export_path</span><span class='p'>:</span> <span class='n'>Path</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>,</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Iterable</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param samples:</span>
+<span class='sd'>    :type samples:</span>
+<span class='sd'>    :param sampling_rate:</span>
+<span class='sd'>    :type sampling_rate:</span>
+<span class='sd'>    :param export_path:</span>
+<span class='sd'>    :type export_path:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>samples</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>(</span><span class='n'>min_length_truncate_batch</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>))</span>
+    <span class='n'>mixed</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sum</span><span class='p'>(</span><span class='n'>samples</span> <span class='o'>/</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>max</span><span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>abs</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>)),</span> <span class='mi'>0</span><span class='p'>)</span>
+    <span class='k'>if</span> <span class='n'>export_path</span><span class='p'>:</span>
+        <span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>save</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='n'>export_path</span><span class='p'>),</span> <span class='n'>to_tensor</span><span class='p'>(</span><span class='n'>mixed</span><span class='p'>),</span> <span class='n'>sampling_rate</span><span class='p'>)</span>
+    <span class='k'>return</span> <span class='n'>mixed</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+
+    <span class='k'>def</span> <span class='nf'>main</span><span class='p'>():</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='kn'>from</span> <span class='nn'>modulation.data.audio.speech.recognition.libri_speech</span> <span class='kn'>import</span> <span class='n'>LibriSpeech</span>
+        <span class='kn'>from</span> <span class='nn'>draugr.visualisation</span> <span class='kn'>import</span> <span class='n'>dissected_channel_plot</span>
+
+        <span class='n'>samples</span> <span class='o'>=</span> <span class='mi'>4</span>
+        <span class='kn'>from</span> <span class='nn'>matplotlib</span> <span class='kn'>import</span> <span class='n'>pyplot</span>
+
+        <span class='n'>libri_speech</span> <span class='o'>=</span> <span class='n'>LibriSpeech</span><span class='p'>(</span><span class='n'>path</span><span class='o'>=</span><span class='n'>Path</span><span class='o'>.</span><span class='n'>home</span><span class='p'>()</span> <span class='o'>/</span> <span class='s2'>&quot;Data&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Audio&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Speech&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;LibriSpeech&quot;</span><span class='p'>)</span>
+        <span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span> <span class='o'>=</span> <span class='nb'>zip</span><span class='p'>(</span><span class='o'>*</span><span class='p'>[(</span><span class='n'>v</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span><span class='o'>.</span><span class='n'>numpy</span><span class='p'>(),</span> <span class='n'>v</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>])</span> <span class='k'>for</span> <span class='n'>_</span><span class='p'>,</span> <span class='n'>v</span> <span class='ow'>in</span> <span class='nb'>zip</span><span class='p'>(</span><span class='nb'>range</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>),</span> <span class='n'>libri_speech</span><span class='p'>)])</span>
+        <span class='k'>assert</span> <span class='nb'>all</span><span class='p'>([</span><span class='n'>sr</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span> <span class='o'>==</span> <span class='n'>s</span> <span class='k'>for</span> <span class='n'>s</span> <span class='ow'>in</span> <span class='n'>sr</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:]])</span>
+        <span class='n'>babble</span> <span class='o'>=</span> <span class='n'>generate_babble_noise</span><span class='p'>(</span><span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>],</span> <span class='n'>export_path</span><span class='o'>=</span><span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='s2'>&quot;babble.wav&quot;</span><span class='p'>)</span>
+
+        <span class='n'>c_names</span> <span class='o'>=</span> <span class='p'>(</span><span class='o'>*</span><span class='p'>(</span><span class='sa'>f</span><span class='s2'>&quot;C</span><span class='si'>{</span><span class='n'>i</span><span class='si'>}</span><span class='s2'>&quot;</span> <span class='k'>for</span> <span class='n'>i</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>files</span><span class='p'>))),</span> <span class='s2'>&quot;mixed&quot;</span><span class='p'>)</span>
+        <span class='n'>files</span> <span class='o'>=</span> <span class='p'>(</span><span class='o'>*</span><span class='n'>files</span><span class='p'>,</span> <span class='n'>babble</span><span class='p'>)</span>
+        <span class='n'>files</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>(</span><span class='n'>min_length_truncate_batch</span><span class='p'>(</span><span class='n'>files</span><span class='p'>))</span><span class='o'>.</span><span class='n'>squeeze</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>)</span>
+
+        <span class='n'>dissected_channel_plot</span><span class='p'>(</span><span class='n'>files</span><span class='p'>,</span> <span class='n'>title</span><span class='o'>=</span><span class='sa'>f</span><span class='s2'>&quot;</span><span class='si'>{</span><span class='n'>samples</span><span class='si'>}</span><span class='s2'> sample babble&quot;</span><span class='p'>,</span> <span class='n'>channel_names</span><span class='o'>=</span><span class='n'>c_names</span><span class='p'>)</span>
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>show</span><span class='p'>()</span>
+
+    <span class='n'>main</span><span class='p'>()</span>
+</pre>
+            </div>
+
+          </div>
+
+        </div>
+      </div>
+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
+        <div class='sphinxsidebarwrapper'>
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+
+
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+          <ul>
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+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../getting_started.html'>Getting Started</a></li>
+          </ul>
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+
+            <h1>Source code for modulation.noise_generation.gaussian_noise</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 10-12-2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>from</span> <span class='nn'>enum</span> <span class='kn'>import</span> <span class='n'>Enum</span>
+<span class='kn'>from</span> <span class='nn'>functools</span> <span class='kn'>import</span> <span class='n'>partial</span>
+<span class='kn'>from</span> <span class='nn'>pathlib</span> <span class='kn'>import</span> <span class='n'>Path</span>
+
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>import</span> <span class='nn'>torchaudio</span>
+<span class='kn'>from</span> <span class='nn'>draugr.numpy_utilities</span> <span class='kn'>import</span> <span class='n'>get_sampler</span><span class='p'>,</span> <span class='n'>normalise_signal</span>
+<span class='kn'>from</span> <span class='nn'>draugr.torch_utilities</span> <span class='kn'>import</span> <span class='n'>to_tensor</span>
+<span class='kn'>from</span> <span class='nn'>matplotlib</span> <span class='kn'>import</span> <span class='n'>pyplot</span>
+<span class='kn'>from</span> <span class='nn'>numpy.fft</span> <span class='kn'>import</span> <span class='n'>irfft</span><span class='p'>,</span> <span class='n'>rfft</span>
+
+
+<div class='viewcode-block' id='white_noise'><a class='viewcode-back' href='../../../generated/modulation.noise_generation.gaussian_noise.white_noise.html#modulation.noise_generation.gaussian_noise.white_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>white_noise</span><span class='p'>(</span><span class='n'>length</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+  <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>  White noise.</span>
+
+<span class='sd'>  * N: Amount of samples.</span>
+
+<span class='sd'>  White noise has a constant power density.</span>
+<span class='sd'>  Its narrowband spectrum is therefore flat.</span>
+<span class='sd'>  The power in white noise will increase by a factor of two for each octave band,</span>
+<span class='sd'>  and therefore increases with 3 dB per octave.</span>
+
+<span class='sd'>  &quot;&quot;&quot;</span>
+  <span class='k'>if</span> <span class='n'>seed</span><span class='p'>:</span>
+    <span class='n'>numpy</span><span class='o'>.</span><span class='n'>random</span><span class='o'>.</span><span class='n'>seed</span><span class='p'>(</span><span class='n'>seed</span><span class='p'>)</span>
+  <span class='k'>return</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>random</span><span class='o'>.</span><span class='n'>randn</span><span class='p'>(</span><span class='n'>length</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='blue_noise'><a class='viewcode-back' href='../../../generated/modulation.noise_generation.gaussian_noise.blue_noise.html#modulation.noise_generation.gaussian_noise.blue_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>blue_noise</span><span class='p'>(</span><span class='n'>length</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+  <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>  Blue noise.</span>
+
+<span class='sd'>  * N: Amount of samples.</span>
+
+<span class='sd'>  Power increases with 6 dB per octave.</span>
+<span class='sd'>  Power density increases with 3 dB per octave.</span>
+<span class='sd'>  &quot;&quot;&quot;</span>
+  <span class='n'>x</span> <span class='o'>=</span> <span class='n'>rfft</span><span class='p'>(</span><span class='n'>white_noise</span><span class='p'>(</span><span class='n'>length</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>))</span> <span class='o'>/</span> <span class='n'>length</span>
+
+  <span class='k'>return</span> <span class='n'>normalise_signal</span><span class='p'>(</span><span class='n'>irfft</span><span class='p'>(</span><span class='n'>x</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sqrt</span><span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>arange</span><span class='p'>(</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>x</span><span class='p'>))))</span><span class='o'>.</span><span class='n'>real</span><span class='p'>[:</span><span class='n'>length</span><span class='p'>])</span>  <span class='c1'># Filter</span></div>
+
+
+<div class='viewcode-block' id='brown_noise'><a class='viewcode-back' href='../../../generated/modulation.noise_generation.gaussian_noise.brown_noise.html#modulation.noise_generation.gaussian_noise.brown_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>brown_noise</span><span class='p'>(</span><span class='n'>length</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+  <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>  Brown noise.</span>
+
+<span class='sd'>  * N: Amount of samples.</span>
+
+<span class='sd'>  Power decreases with -3 dB per octave.</span>
+<span class='sd'>  Power density decreases with 6 dB per octave.</span>
+
+<span class='sd'>  &quot;&quot;&quot;</span>
+  <span class='n'>x</span> <span class='o'>=</span> <span class='n'>rfft</span><span class='p'>(</span><span class='n'>white_noise</span><span class='p'>(</span><span class='n'>length</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>))</span> <span class='o'>/</span> <span class='n'>length</span>
+  <span class='k'>return</span> <span class='n'>normalise_signal</span><span class='p'>(</span><span class='n'>irfft</span><span class='p'>(</span><span class='n'>x</span> <span class='o'>/</span> <span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>arange</span><span class='p'>(</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>x</span><span class='p'>))</span> <span class='o'>+</span> <span class='mi'>1</span><span class='p'>))</span><span class='o'>.</span><span class='n'>real</span><span class='p'>[:</span><span class='n'>length</span><span class='p'>])</span>  <span class='c1'># Filter</span></div>
+
+
+<div class='viewcode-block' id='pink_noise'><a class='viewcode-back' href='../../../generated/modulation.noise_generation.gaussian_noise.pink_noise.html#modulation.noise_generation.gaussian_noise.pink_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>pink_noise</span><span class='p'>(</span><span class='n'>length</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+  <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>  Pink noise.</span>
+
+<span class='sd'>  :param length: Amount of samples.</span>
+<span class='sd'>  :param seed: State of PRNG.</span>
+<span class='sd'>  :type seed: :class:`numpy.random.RandomState`</span>
+
+<span class='sd'>  Pink noise has equal power in bands that are proportionally wide.</span>
+<span class='sd'>  Power density decreases with 3 dB per octave.</span>
+
+<span class='sd'>  &quot;&quot;&quot;</span>
+  <span class='c1'># This method uses the filter with the following coefficients.</span>
+  <span class='c1'># b = numpy.array([0.049922035, -0.095993537, 0.050612699, -0.004408786])</span>
+  <span class='c1'># a = numpy.array([1, -2.494956002, 2.017265875, -0.522189400])</span>
+  <span class='c1'># return lfilter(B, A, numpy.random.randn(N))</span>
+  <span class='c1'># Another way would be using the FFT</span>
+  <span class='c1'># x = numpy.random.randn(N)</span>
+  <span class='c1'># X = rfft(x) / N</span>
+  <span class='n'>sampler</span> <span class='o'>=</span> <span class='n'>get_sampler</span><span class='p'>(</span><span class='n'>seed</span><span class='p'>)</span>
+  <span class='n'>uneven</span> <span class='o'>=</span> <span class='n'>length</span> <span class='o'>%</span> <span class='mi'>2</span>
+  <span class='n'>x</span> <span class='o'>=</span> <span class='n'>sampler</span><span class='o'>.</span><span class='n'>randn</span><span class='p'>(</span><span class='n'>length</span> <span class='o'>//</span> <span class='mi'>2</span> <span class='o'>+</span> <span class='mi'>1</span> <span class='o'>+</span> <span class='n'>uneven</span><span class='p'>)</span> <span class='o'>+</span> <span class='mi'>1</span><span class='n'>j</span> <span class='o'>*</span> <span class='n'>sampler</span><span class='o'>.</span><span class='n'>randn</span><span class='p'>(</span><span class='n'>length</span> <span class='o'>//</span> <span class='mi'>2</span> <span class='o'>+</span> <span class='mi'>1</span> <span class='o'>+</span> <span class='n'>uneven</span><span class='p'>)</span>
+  <span class='n'>y</span> <span class='o'>=</span> <span class='p'>(</span><span class='n'>irfft</span><span class='p'>(</span><span class='n'>x</span> <span class='o'>/</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sqrt</span><span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>arange</span><span class='p'>(</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>x</span><span class='p'>))</span> <span class='o'>+</span> <span class='mf'>1.0</span><span class='p'>)))</span><span class='o'>.</span><span class='n'>real</span>  <span class='c1'># +1 to avoid divide by zero</span>
+  <span class='k'>if</span> <span class='n'>uneven</span><span class='p'>:</span>
+    <span class='n'>y</span> <span class='o'>=</span> <span class='n'>y</span><span class='p'>[:</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>]</span>
+  <span class='k'>return</span> <span class='n'>normalise_signal</span><span class='p'>(</span><span class='n'>y</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='violet_noise'><a class='viewcode-back' href='../../../generated/modulation.noise_generation.gaussian_noise.violet_noise.html#modulation.noise_generation.gaussian_noise.violet_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>violet_noise</span><span class='p'>(</span><span class='n'>length</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+  <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>  Violet noise. Power increases with 6 dB per octave.</span>
+
+<span class='sd'>  :param length: Amount of samples.</span>
+<span class='sd'>  :param seed: State of PRNG.</span>
+<span class='sd'>  :type seed: :class:`numpy.random.RandomState`</span>
+
+<span class='sd'>  Power increases with +9 dB per octave.</span>
+<span class='sd'>  Power density increases with +6 dB per octave.</span>
+
+<span class='sd'>  &quot;&quot;&quot;</span>
+  <span class='n'>sampler</span> <span class='o'>=</span> <span class='n'>get_sampler</span><span class='p'>(</span><span class='n'>seed</span><span class='p'>)</span>
+  <span class='n'>uneven</span> <span class='o'>=</span> <span class='n'>length</span> <span class='o'>%</span> <span class='mi'>2</span>
+  <span class='n'>x</span> <span class='o'>=</span> <span class='n'>sampler</span><span class='o'>.</span><span class='n'>randn</span><span class='p'>(</span><span class='n'>length</span> <span class='o'>//</span> <span class='mi'>2</span> <span class='o'>+</span> <span class='mi'>1</span> <span class='o'>+</span> <span class='n'>uneven</span><span class='p'>)</span> <span class='o'>+</span> <span class='mi'>1</span><span class='n'>j</span> <span class='o'>*</span> <span class='n'>sampler</span><span class='o'>.</span><span class='n'>randn</span><span class='p'>(</span><span class='n'>length</span> <span class='o'>//</span> <span class='mi'>2</span> <span class='o'>+</span> <span class='mi'>1</span> <span class='o'>+</span> <span class='n'>uneven</span><span class='p'>)</span>
+  <span class='n'>y</span> <span class='o'>=</span> <span class='p'>(</span><span class='n'>irfft</span><span class='p'>(</span><span class='n'>x</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>arange</span><span class='p'>(</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>x</span><span class='p'>))))</span><span class='o'>.</span><span class='n'>real</span>  <span class='c1'># Filter</span>
+  <span class='k'>if</span> <span class='n'>uneven</span><span class='p'>:</span>
+    <span class='n'>y</span> <span class='o'>=</span> <span class='n'>y</span><span class='p'>[:</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>]</span>
+  <span class='k'>return</span> <span class='n'>normalise_signal</span><span class='p'>(</span><span class='n'>y</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='GaussianNoiseTypeEnum'><a class='viewcode-back' href='../../../generated/modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.html#modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum'>[docs]</a><span class='k'>class</span> <span class='nc'>GaussianNoiseTypeEnum</span><span class='p'>(</span><span class='n'>Enum</span><span class='p'>):</span>
+  <span class='sa'>r</span><span class='sd'>&quot;&quot;&quot; description &quot;&quot;&quot;</span>
+  <span class='n'>white</span> <span class='o'>=</span> <span class='n'>partial</span><span class='p'>(</span><span class='n'>white_noise</span><span class='p'>)</span>  <span class='c1'># Partial to workaround interpreting as a method definition</span>
+  <span class='n'>brown</span> <span class='o'>=</span> <span class='n'>partial</span><span class='p'>(</span><span class='n'>brown_noise</span><span class='p'>)</span>
+  <span class='n'>blue</span> <span class='o'>=</span> <span class='n'>partial</span><span class='p'>(</span><span class='n'>blue_noise</span><span class='p'>)</span>
+  <span class='n'>pink</span> <span class='o'>=</span> <span class='n'>partial</span><span class='p'>(</span><span class='n'>pink_noise</span><span class='p'>)</span>
+  <span class='n'>violet</span> <span class='o'>=</span> <span class='n'>partial</span><span class='p'>(</span><span class='n'>violet_noise</span><span class='p'>)</span>
+
+  <span class='k'>def</span> <span class='fm'>__call__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='o'>*</span><span class='n'>args</span><span class='p'>,</span> <span class='o'>**</span><span class='n'>kwargs</span><span class='p'>):</span>
+    <span class='k'>return</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>value</span><span class='p'>(</span><span class='o'>*</span><span class='n'>args</span><span class='p'>,</span> <span class='o'>**</span><span class='n'>kwargs</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='generate_noise'><a class='viewcode-back' href='../../../generated/modulation.noise_generation.gaussian_noise.generate_noise.html#modulation.noise_generation.gaussian_noise.generate_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>generate_noise</span><span class='p'>(</span>
+    <span class='n'>length</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span>
+    <span class='o'>*</span><span class='p'>,</span>
+    <span class='n'>seed</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>,</span>
+    <span class='n'>noise_type</span><span class='p'>:</span> <span class='n'>GaussianNoiseTypeEnum</span> <span class='o'>=</span> <span class='n'>GaussianNoiseTypeEnum</span><span class='o'>.</span><span class='n'>white</span><span class='p'>,</span>
+    <span class='n'>export_path</span><span class='p'>:</span> <span class='n'>Path</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>,</span>
+    <span class='n'>sampling_rate</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='mi'>16000</span><span class='p'>,</span>
+    <span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+  <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>  :param length:</span>
+<span class='sd'>  :type length:</span>
+<span class='sd'>  :param seed:</span>
+<span class='sd'>  :type seed:</span>
+<span class='sd'>  :param noise_type:</span>
+<span class='sd'>  :type noise_type:</span>
+<span class='sd'>  :param export_path:</span>
+<span class='sd'>  :type export_path:</span>
+<span class='sd'>  :param sampling_rate:</span>
+<span class='sd'>  :type sampling_rate:</span>
+<span class='sd'>  :return:</span>
+<span class='sd'>  :rtype:</span>
+<span class='sd'>  &quot;&quot;&quot;</span>
+  <span class='n'>normalised</span> <span class='o'>=</span> <span class='n'>noise_type</span><span class='p'>(</span><span class='n'>length</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>)</span>
+  <span class='k'>if</span> <span class='n'>export_path</span><span class='p'>:</span>
+    <span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>save</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='n'>export_path</span><span class='p'>),</span> <span class='n'>to_tensor</span><span class='p'>(</span><span class='n'>normalised</span><span class='p'>),</span> <span class='n'>sampling_rate</span><span class='p'>)</span>
+  <span class='k'>return</span> <span class='n'>normalised</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+
+  <span class='k'>def</span> <span class='nf'>asijsda</span><span class='p'>():</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>sampling_rate</span> <span class='o'>=</span> <span class='mi'>16000</span>
+    <span class='n'>length_sec</span> <span class='o'>=</span> <span class='mi'>5</span>
+    <span class='n'>noises</span> <span class='o'>=</span> <span class='p'>{}</span>
+    <span class='k'>for</span> <span class='n'>a</span> <span class='ow'>in</span> <span class='n'>GaussianNoiseTypeEnum</span><span class='p'>:</span>
+      <span class='n'>name</span> <span class='o'>=</span> <span class='sa'>f</span><span class='s2'>&quot;</span><span class='si'>{</span><span class='n'>a</span><span class='o'>.</span><span class='n'>name</span><span class='si'>}</span><span class='s2'>_noise.wav&quot;</span>
+
+      <span class='n'>noise</span> <span class='o'>=</span> <span class='n'>generate_noise</span><span class='p'>(</span>
+          <span class='n'>sampling_rate</span> <span class='o'>*</span> <span class='n'>length_sec</span><span class='p'>,</span>
+          <span class='n'>seed</span> <span class='o'>=</span> <span class='mi'>42</span><span class='p'>,</span>
+          <span class='n'>noise_type</span> <span class='o'>=</span> <span class='n'>a</span><span class='p'>,</span>
+          <span class='n'>export_path</span> <span class='o'>=</span> <span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='n'>name</span><span class='p'>,</span>
+          <span class='p'>)</span>
+      <span class='n'>noises</span><span class='p'>[</span><span class='n'>name</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>noise</span>
+
+    <span class='n'>channels</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>(</span><span class='nb'>list</span><span class='p'>(</span><span class='n'>noises</span><span class='o'>.</span><span class='n'>values</span><span class='p'>()))</span>
+    <span class='n'>channels</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>concatenate</span><span class='p'>([</span><span class='n'>channels</span><span class='p'>,</span> <span class='n'>normalise_signal</span><span class='p'>(</span><span class='n'>channels</span><span class='o'>.</span><span class='n'>sum</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='n'>keepdims</span> <span class='o'>=</span> <span class='kc'>True</span><span class='p'>))])</span>
+    <span class='n'>channel_names</span> <span class='o'>=</span> <span class='nb'>list</span><span class='p'>(</span><span class='n'>noises</span><span class='o'>.</span><span class='n'>keys</span><span class='p'>())</span> <span class='o'>+</span> <span class='p'>[</span><span class='s2'>&quot;mixed&quot;</span><span class='p'>]</span>
+    <span class='kn'>from</span> <span class='nn'>draugr</span> <span class='kn'>import</span> <span class='n'>dissected_channel_plot</span>
+
+    <span class='n'>dissected_channel_plot</span><span class='p'>(</span>
+        <span class='n'>channels</span><span class='p'>,</span>
+        <span class='n'>channel_names</span> <span class='o'>=</span> <span class='n'>channel_names</span><span class='p'>,</span>
+        <span class='n'>sampling_rate</span> <span class='o'>=</span> <span class='n'>sampling_rate</span><span class='p'>,</span>
+        <span class='n'>max_resolution</span> <span class='o'>=</span> <span class='mi'>16000</span><span class='p'>,</span>
+        <span class='p'>)</span>
+    <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>show</span><span class='p'>()</span>
+
+    <span class='n'>asijsda</span><span class='p'>()</span>
+</pre>
+            </div>
+
+          </div>
+
+        </div>
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../getting_started.html'>Getting Started</a></li>
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+
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+            <h3>Related Topics</h3>
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+              <li><a href='../../../index.html'>Documentation overview</a>
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+          <div class='body' role='main'>
+
+            <h1>Source code for modulation.noise_generation.shaped_noise</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 09-12-2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>from</span> <span class='nn'>pathlib</span> <span class='kn'>import</span> <span class='n'>Path</span>
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Iterable</span>
+
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>import</span> <span class='nn'>torchaudio</span>
+<span class='kn'>from</span> <span class='nn'>warg</span> <span class='kn'>import</span> <span class='n'>next_pow_2</span>
+<span class='kn'>from</span> <span class='nn'>draugr.numpy_utilities</span> <span class='kn'>import</span> <span class='n'>zero_pad_to_power_2</span>
+<span class='kn'>from</span> <span class='nn'>draugr.random_utilities.seeding</span> <span class='kn'>import</span> <span class='n'>numpy_seed</span>
+<span class='kn'>from</span> <span class='nn'>draugr.torch_utilities</span> <span class='kn'>import</span> <span class='n'>to_tensor</span>
+<span class='kn'>from</span> <span class='nn'>matplotlib</span> <span class='kn'>import</span> <span class='n'>pyplot</span>
+<span class='kn'>from</span> <span class='nn'>numpy</span> <span class='kn'>import</span> <span class='n'>fft</span>
+<span class='kn'>from</span> <span class='nn'>scipy.signal</span> <span class='kn'>import</span> <span class='n'>butter</span><span class='p'>,</span> <span class='n'>filtfilt</span><span class='p'>,</span> <span class='n'>welch</span>
+
+<span class='kn'>from</span> <span class='nn'>modulation.audio_utilities.funcs</span> <span class='kn'>import</span> <span class='n'>hilbert_envelope</span>
+
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[</span><span class='s2'>&quot;generate_speech_shaped_noise&quot;</span><span class='p'>]</span>
+
+
+<span class='k'>def</span> <span class='nf'>spectrum_like_noise</span><span class='p'>(</span>
+    <span class='n'>signal</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span>
+    <span class='o'>*</span><span class='p'>,</span>
+    <span class='n'>sampling_rate</span><span class='o'>=</span><span class='mi'>40000</span><span class='p'>,</span>
+    <span class='n'>keep_signal_amp_envelope</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>,</span>
+    <span class='n'>low_pass_cutoff</span><span class='o'>=</span><span class='mi'>50</span><span class='p'>,</span>  <span class='c1'># Hz</span>
+    <span class='n'>low_pass_order</span><span class='o'>=</span><span class='mi'>6</span><span class='p'>,</span>
+    <span class='n'>seed</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='mi'>42</span><span class='p'>,</span>
+    <span class='n'>window_length_sec</span><span class='p'>:</span> <span class='nb'>float</span> <span class='o'>=</span> <span class='mi'>20</span> <span class='o'>/</span> <span class='mi'>1000</span><span class='p'>,</span>  <span class='c1'># 20 ms</span>
+    <span class='n'>p_overlap</span><span class='p'>:</span> <span class='nb'>float</span> <span class='o'>=</span> <span class='mf'>0.5</span><span class='p'>,</span>
+    <span class='n'>long_term_avg</span><span class='p'>:</span> <span class='nb'>bool</span> <span class='o'>=</span> <span class='kc'>True</span><span class='p'>,</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;Create a noise with same spectrum as the input signal.</span>
+<span class='sd'>    randomises phase</span>
+
+<span class='sd'>    Parameters</span>
+<span class='sd'>    ----------</span>
+<span class='sd'>    signal : array_like</span>
+<span class='sd'>        Input signal.</span>
+<span class='sd'>    sampling_rate : int</span>
+<span class='sd'>         Sampling frequency of the input signal. (Default value = 40000)</span>
+<span class='sd'>    keep_signal_amp_envelope : bool</span>
+<span class='sd'>         Apply the envelope of the original signal to the noise. (Default</span>
+<span class='sd'>         value = False)</span>
+<span class='sd'>    low_pass_cutoff : float</span>
+<span class='sd'>    low_pass_order : int</span>
+<span class='sd'>    seed : int</span>
+<span class='sd'>    long_term_avg : bool</span>
+<span class='sd'>    window_length_sec: int</span>
+<span class='sd'>    p_overlap: float</span>
+
+
+<span class='sd'>    Returns</span>
+<span class='sd'>    -------</span>
+<span class='sd'>    ndarray</span>
+<span class='sd'>        Noise signal.</span>
+
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>assert</span> <span class='n'>window_length_sec</span> <span class='o'>&gt;</span> <span class='mi'>0</span>
+    <span class='k'>assert</span> <span class='mi'>0</span> <span class='o'>&lt;=</span> <span class='n'>p_overlap</span> <span class='o'>&lt;=</span> <span class='mi'>1</span>
+    <span class='n'>signal</span> <span class='o'>=</span> <span class='n'>zero_pad_to_power_2</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>)</span>  <span class='c1'># Ensure welch works with any window size</span>
+    <span class='n'>signal_length</span> <span class='o'>=</span> <span class='n'>signal</span><span class='o'>.</span><span class='n'>shape</span><span class='p'>[</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>]</span>
+    <span class='n'>window_sum_squares</span> <span class='o'>=</span> <span class='n'>signal_length</span>  <span class='c1'># scaling factor defined as sum of squared samples of window function</span>
+    <span class='n'>sc</span> <span class='o'>=</span> <span class='mi'>2</span> <span class='o'>/</span> <span class='p'>(</span>
+        <span class='n'>sampling_rate</span> <span class='o'>*</span> <span class='n'>window_sum_squares</span>
+    <span class='p'>)</span>  <span class='c1'># Scaling coefficient 2 takes into account removal of energy at negative frequencies (we drop this side of PSD)</span>
+
+    <span class='k'>if</span> <span class='ow'>not</span> <span class='n'>long_term_avg</span><span class='p'>:</span>
+        <span class='n'>n_fft</span> <span class='o'>=</span> <span class='n'>next_pow_2</span><span class='p'>(</span><span class='n'>signal_length</span><span class='p'>)</span>
+        <span class='n'>spec</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>abs</span><span class='p'>(</span><span class='n'>fft</span><span class='o'>.</span><span class='n'>rfft</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>,</span> <span class='n'>n_fft</span><span class='p'>))</span>
+        <span class='n'>psd</span> <span class='o'>=</span> <span class='p'>(</span><span class='n'>spec</span><span class='o'>**</span><span class='mi'>2</span><span class='p'>)</span> <span class='o'>*</span> <span class='n'>sc</span>
+    <span class='k'>else</span><span class='p'>:</span>
+        <span class='n'>n_per_seg</span> <span class='o'>=</span> <span class='n'>next_pow_2</span><span class='p'>(</span><span class='nb'>int</span><span class='p'>(</span><span class='n'>sampling_rate</span> <span class='o'>*</span> <span class='n'>window_length_sec</span><span class='p'>))</span>  <span class='c1'># next_pow_2 per seg == n_fft</span>
+        <span class='n'>n_overlap</span> <span class='o'>=</span> <span class='nb'>int</span><span class='p'>(</span><span class='n'>n_per_seg</span> <span class='o'>*</span> <span class='n'>p_overlap</span><span class='p'>)</span>
+        <span class='n'>f</span><span class='p'>,</span> <span class='n'>psd</span> <span class='o'>=</span> <span class='n'>welch</span><span class='p'>(</span>
+            <span class='n'>signal</span><span class='p'>,</span>
+            <span class='n'>sampling_rate</span><span class='p'>,</span>
+            <span class='n'>nperseg</span><span class='o'>=</span><span class='n'>n_per_seg</span><span class='p'>,</span>
+            <span class='n'>noverlap</span><span class='o'>=</span><span class='n'>n_overlap</span><span class='p'>,</span>
+            <span class='n'>scaling</span><span class='o'>=</span><span class='s2'>&quot;density&quot;</span><span class='p'>,</span>
+            <span class='n'>return_onesided</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>,</span>
+            <span class='n'>detrend</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>,</span>
+            <span class='c1'># window=&#39;boxcar&#39;,</span>
+            <span class='n'>window</span><span class='o'>=</span><span class='s2'>&quot;hanning&quot;</span><span class='p'>,</span>
+        <span class='p'>)</span>
+        <span class='n'>n_fft</span> <span class='o'>=</span> <span class='n'>n_per_seg</span>
+        <span class='n'>psd</span> <span class='o'>/=</span> <span class='n'>signal_length</span> <span class='o'>/</span> <span class='n'>n_per_seg</span>  <span class='c1'># normalise?</span>
+    <span class='n'>spec</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sqrt</span><span class='p'>((</span><span class='n'>psd</span> <span class='o'>/</span> <span class='n'>sc</span><span class='p'>))</span>
+
+    <span class='n'>noise</span> <span class='o'>=</span> <span class='p'>[]</span>
+    <span class='n'>runs</span> <span class='o'>=</span> <span class='n'>signal_length</span> <span class='o'>//</span> <span class='n'>n_fft</span>
+    <span class='k'>for</span> <span class='n'>i</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='n'>runs</span> <span class='o'>+</span> <span class='mi'>1</span><span class='p'>):</span>
+        <span class='n'>numpy_seed</span><span class='p'>(</span><span class='n'>seed</span> <span class='o'>+</span> <span class='n'>i</span><span class='p'>)</span>
+        <span class='n'>noise</span><span class='o'>.</span><span class='n'>extend</span><span class='p'>(</span>
+            <span class='n'>numpy</span><span class='o'>.</span><span class='n'>real</span><span class='p'>(</span>
+                <span class='n'>fft</span><span class='o'>.</span><span class='n'>irfft</span><span class='p'>(</span>
+                    <span class='n'>spec</span>
+                    <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>exp</span><span class='p'>(</span>
+                        <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>pi</span> <span class='o'>*</span> <span class='mi'>1</span><span class='n'>j</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>random</span><span class='o'>.</span><span class='n'>random</span><span class='p'>(</span><span class='n'>spec</span><span class='o'>.</span><span class='n'>shape</span><span class='p'>[</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>])</span>
+                    <span class='p'>),</span>  <span class='c1'># Randomise phase. 0-&gt;360, 2 pi rads</span>
+                    <span class='n'>n_fft</span><span class='p'>,</span>
+                <span class='p'>)</span>
+            <span class='p'>)</span>
+        <span class='p'>)</span>  <span class='c1'># Give each spectral component a random phase, PHI(f(k)) = random number,</span>
+        <span class='c1'># uniformly distributed between 0 and 360 degrees (or equivalently, between 0 and 2Pi radians);</span>
+
+    <span class='n'>noise</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>(</span><span class='n'>noise</span><span class='p'>)[:</span><span class='n'>signal_length</span><span class='p'>]</span>
+
+    <span class='k'>if</span> <span class='n'>keep_signal_amp_envelope</span><span class='p'>:</span>
+        <span class='p'>[</span><span class='n'>bb</span><span class='p'>,</span> <span class='n'>aa</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>butter</span><span class='p'>(</span><span class='n'>low_pass_order</span><span class='p'>,</span> <span class='n'>low_pass_cutoff</span> <span class='o'>/</span> <span class='p'>(</span><span class='n'>sampling_rate</span> <span class='o'>/</span> <span class='mi'>2</span><span class='p'>))</span>  <span class='c1'># Cutoff Hz, LP filter</span>
+        <span class='n'>noise</span> <span class='o'>*=</span> <span class='n'>filtfilt</span><span class='p'>(</span>
+            <span class='n'>bb</span><span class='p'>,</span>  <span class='c1'># numerator</span>
+            <span class='n'>aa</span><span class='p'>,</span>  <span class='c1'># denominator</span>
+            <span class='n'>hilbert_envelope</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>),</span>  <span class='c1'># envelope of speech signal in time domain</span>
+        <span class='p'>)</span>
+
+    <span class='k'>return</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>expand_dims</span><span class='p'>(</span><span class='n'>noise</span><span class='p'>,</span> <span class='mi'>0</span><span class='p'>)</span>
+
+
+<div class='viewcode-block' id='generate_speech_shaped_noise'><a class='viewcode-back' href='../../../generated/modulation.noise_generation.shaped_noise.generate_speech_shaped_noise.html#modulation.noise_generation.shaped_noise.generate_speech_shaped_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>generate_speech_shaped_noise</span><span class='p'>(</span>
+    <span class='n'>samples</span><span class='p'>:</span> <span class='n'>Iterable</span><span class='p'>[</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>],</span>
+    <span class='n'>sampling_rate</span><span class='p'>,</span>
+    <span class='o'>*</span><span class='p'>,</span>
+    <span class='n'>export_path</span><span class='p'>:</span> <span class='n'>Path</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>,</span>
+    <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>,</span>
+    <span class='n'>window_length_sec</span><span class='o'>=</span><span class='mi'>20</span> <span class='o'>/</span> <span class='mi'>1000</span><span class='p'>,</span>
+    <span class='n'>p_overlap</span><span class='o'>=</span><span class='mf'>0.5</span><span class='p'>,</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Iterable</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    White (flat-spectrum) noise has more energy at high frequencies, and therefore produces more masking of the higher formants and frication noises than speech-shaped noise, speech shaped noise mask evenly throughout the speech signal spectrum</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+    <span class='n'>samples</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>concatenate</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>,</span> <span class='o'>-</span><span class='mi'>1</span><span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+    <span class='n'>noise</span> <span class='o'>=</span> <span class='n'>spectrum_like_noise</span><span class='p'>(</span>
+        <span class='n'>samples</span><span class='p'>,</span>
+        <span class='n'>sampling_rate</span><span class='o'>=</span><span class='n'>sampling_rate</span><span class='p'>,</span>
+        <span class='n'>long_term_avg</span><span class='o'>=</span><span class='n'>long_term_avg</span><span class='p'>,</span>
+        <span class='n'>window_length_sec</span><span class='o'>=</span><span class='n'>window_length_sec</span><span class='p'>,</span>
+        <span class='n'>p_overlap</span><span class='o'>=</span><span class='n'>p_overlap</span><span class='p'>,</span>
+    <span class='p'>)</span>
+    <span class='k'>if</span> <span class='n'>export_path</span><span class='p'>:</span>
+        <span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>save</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='n'>export_path</span><span class='p'>),</span> <span class='n'>to_tensor</span><span class='p'>(</span><span class='n'>noise</span><span class='p'>),</span> <span class='n'>sampling_rate</span><span class='p'>)</span>
+    <span class='k'>return</span> <span class='n'>noise</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+
+    <span class='k'>def</span> <span class='nf'>reald</span><span class='p'>():</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='kn'>from</span> <span class='nn'>draugr.visualisation</span> <span class='kn'>import</span> <span class='n'>ltass_plot</span>
+        <span class='kn'>from</span> <span class='nn'>modulation.data.audio.speech.recognition.libri_speech</span> <span class='kn'>import</span> <span class='n'>LibriSpeech</span>
+
+        <span class='n'>libri_speech</span> <span class='o'>=</span> <span class='n'>LibriSpeech</span><span class='p'>(</span><span class='n'>path</span><span class='o'>=</span><span class='n'>Path</span><span class='o'>.</span><span class='n'>home</span><span class='p'>()</span> <span class='o'>/</span> <span class='s2'>&quot;Data&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Audio&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Speech&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;LibriSpeech&quot;</span><span class='p'>)</span>
+        <span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span> <span class='o'>=</span> <span class='nb'>zip</span><span class='p'>(</span><span class='o'>*</span><span class='p'>[(</span><span class='n'>v</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span><span class='o'>.</span><span class='n'>numpy</span><span class='p'>(),</span> <span class='n'>v</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>])</span> <span class='k'>for</span> <span class='n'>_</span><span class='p'>,</span> <span class='n'>v</span> <span class='ow'>in</span> <span class='nb'>zip</span><span class='p'>(</span><span class='nb'>range</span><span class='p'>(</span><span class='mi'>20</span><span class='p'>),</span> <span class='n'>libri_speech</span><span class='p'>)])</span>
+        <span class='k'>assert</span> <span class='nb'>all</span><span class='p'>([</span><span class='n'>sr</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span> <span class='o'>==</span> <span class='n'>s</span> <span class='k'>for</span> <span class='n'>s</span> <span class='ow'>in</span> <span class='n'>sr</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:]])</span>
+        <span class='n'>sr</span> <span class='o'>=</span> <span class='n'>sr</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span>
+
+        <span class='c1'># from modulation.noise_generation import generate_babble_noise</span>
+        <span class='c1'># files = generate_babble_noise(samples, sampling_rate)</span>
+        <span class='n'>samples_len</span> <span class='o'>=</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>concatenate</span><span class='p'>(</span><span class='n'>files</span><span class='p'>,</span> <span class='o'>-</span><span class='mi'>1</span><span class='p'>)[</span><span class='mi'>0</span><span class='p'>])</span>
+
+        <span class='n'>noise_welch</span> <span class='o'>=</span> <span class='n'>generate_speech_shaped_noise</span><span class='p'>(</span>
+            <span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>,</span> <span class='n'>export_path</span><span class='o'>=</span><span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='s2'>&quot;ssn_welch.wav&quot;</span>
+        <span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+        <span class='n'>noise_fft</span> <span class='o'>=</span> <span class='n'>generate_speech_shaped_noise</span><span class='p'>(</span>
+            <span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>,</span> <span class='n'>export_path</span><span class='o'>=</span><span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='s2'>&quot;ssn_fft.wav&quot;</span>
+        <span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+        <span class='c1'># noise_welch_full = generate_speech_shaped_noise(files, sr, long_term_avg=True, window_length_sec= samples_len/sr,p_overlap=0)[0]</span>
+        <span class='n'>files</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>concatenate</span><span class='p'>(</span><span class='n'>files</span><span class='p'>,</span> <span class='o'>-</span><span class='mi'>1</span><span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+        <span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>save</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='s2'>&quot;ssn_signal.wav&quot;</span><span class='p'>),</span> <span class='n'>to_tensor</span><span class='p'>(</span><span class='n'>files</span><span class='p'>),</span> <span class='n'>sr</span><span class='p'>)</span>
+
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;signal&quot;</span><span class='p'>)</span>
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>noise_welch</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;noise_welch&quot;</span><span class='p'>)</span>
+        <span class='c1'># ltass_plot(noise_welch_full, sr, label=&#39;noise_welch_full&#39;)</span>
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>noise_fft</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;noise_fft&quot;</span><span class='p'>)</span>
+
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>legend</span><span class='p'>()</span>
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>show</span><span class='p'>()</span>
+
+    <span class='k'>def</span> <span class='nf'>synth</span><span class='p'>():</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='kn'>from</span> <span class='nn'>draugr.visualisation</span> <span class='kn'>import</span> <span class='n'>ltass_plot</span>
+        <span class='kn'>from</span> <span class='nn'>modulation.data.audio.speech.recognition.libri_speech</span> <span class='kn'>import</span> <span class='n'>LibriSpeech</span>
+
+        <span class='n'>signal_length_sec</span> <span class='o'>=</span> <span class='mi'>11</span>
+        <span class='n'>t</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>linspace</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='n'>signal_length_sec</span><span class='p'>,</span> <span class='nb'>int</span><span class='p'>(</span><span class='mi'>1000</span> <span class='o'>*</span> <span class='n'>signal_length_sec</span><span class='p'>),</span> <span class='n'>endpoint</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>)</span>
+        <span class='n'>signal</span> <span class='o'>=</span> <span class='p'>(</span>
+            <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sin</span><span class='p'>(</span><span class='mi'>50</span> <span class='o'>*</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>pi</span> <span class='o'>*</span> <span class='n'>t</span><span class='p'>)</span> <span class='o'>+</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sin</span><span class='p'>(</span><span class='mi'>200</span> <span class='o'>*</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>pi</span> <span class='o'>*</span> <span class='n'>t</span><span class='p'>)</span> <span class='o'>+</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sin</span><span class='p'>(</span><span class='mi'>400</span> <span class='o'>*</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>numpy</span><span
+                class='o'>.</span><span class='n'>pi</span> <span class='o'>*</span> <span class='n'>t</span><span class='p'>)</span>
+        <span class='p'>)</span>
+        <span class='n'>sr</span> <span class='o'>=</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>t</span><span class='p'>)</span> <span class='o'>/</span> <span class='n'>signal_length_sec</span>
+
+        <span class='n'>noise_welch</span> <span class='o'>=</span> <span class='n'>spectrum_like_noise</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>,</span> <span class='n'>sampling_rate</span><span class='o'>=</span><span class='n'>sr</span><span class='p'>,</span> <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+        <span class='n'>noise_welch_full</span> <span class='o'>=</span> <span class='n'>spectrum_like_noise</span><span class='p'>(</span>
+            <span class='n'>signal</span><span class='p'>,</span>
+            <span class='n'>sampling_rate</span><span class='o'>=</span><span class='n'>sr</span><span class='p'>,</span>
+            <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>,</span>
+            <span class='n'>window_length_sec</span><span class='o'>=</span><span class='n'>signal_length_sec</span><span class='p'>,</span>
+            <span class='n'>p_overlap</span><span class='o'>=</span><span class='mi'>0</span><span class='p'>,</span>
+        <span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+        <span class='n'>noise_fft</span> <span class='o'>=</span> <span class='n'>spectrum_like_noise</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>,</span> <span class='n'>sampling_rate</span><span class='o'>=</span><span class='n'>sr</span><span class='p'>,</span> <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;signal&quot;</span><span class='p'>)</span>
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>noise_welch</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;noise_welch&quot;</span><span class='p'>)</span>
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>noise_welch_full</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;noise_welch_full&quot;</span><span class='p'>)</span>
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>noise_fft</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;noise_fft&quot;</span><span class='p'>)</span>
+
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>legend</span><span class='p'>()</span>
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>show</span><span class='p'>()</span>
+
+    <span class='k'>def</span> <span class='nf'>distinct_real</span><span class='p'>():</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='kn'>from</span> <span class='nn'>draugr.visualisation</span> <span class='kn'>import</span> <span class='n'>ltass_plot</span>
+        <span class='kn'>from</span> <span class='nn'>modulation.data.audio.speech.recognition.libri_speech</span> <span class='kn'>import</span> <span class='n'>LibriSpeech</span>
+
+        <span class='n'>samples</span> <span class='o'>=</span> <span class='mi'>6</span>
+        <span class='n'>d_male</span> <span class='o'>=</span> <span class='nb'>iter</span><span class='p'>(</span>
+            <span class='n'>LibriSpeech</span><span class='p'>(</span>
+                <span class='n'>path</span><span class='o'>=</span><span class='n'>Path</span><span class='o'>.</span><span class='n'>home</span><span class='p'>()</span> <span class='o'>/</span> <span class='s2'>&quot;Data&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Audio&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Speech&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;LibriSpeech&quot;</span><span class='p'>,</span>
+                <span class='n'>custom_subset</span><span class='o'>=</span><span class='n'>LibriSpeech</span><span class='o'>.</span><span class='n'>CustomSubsets</span><span class='o'>.</span><span class='n'>male</span><span class='p'>,</span>
+            <span class='p'>)</span>
+        <span class='p'>)</span>
+        <span class='n'>d_female</span> <span class='o'>=</span> <span class='nb'>iter</span><span class='p'>(</span>
+            <span class='n'>LibriSpeech</span><span class='p'>(</span>
+                <span class='n'>path</span><span class='o'>=</span><span class='n'>Path</span><span class='o'>.</span><span class='n'>home</span><span class='p'>()</span> <span class='o'>/</span> <span class='s2'>&quot;Data&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Audio&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Speech&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;LibriSpeech&quot;</span><span class='p'>,</span>
+                <span class='n'>custom_subset</span><span class='o'>=</span><span class='n'>LibriSpeech</span><span class='o'>.</span><span class='n'>CustomSubsets</span><span class='o'>.</span><span class='n'>female</span><span class='p'>,</span>
+            <span class='p'>)</span>
+        <span class='p'>)</span>
+        <span class='n'>male_unique</span> <span class='o'>=</span> <span class='p'>{}</span>
+        <span class='k'>while</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>male_unique</span><span class='p'>)</span> <span class='o'>&lt;</span> <span class='n'>samples</span> <span class='o'>//</span> <span class='mi'>2</span><span class='p'>:</span>
+            <span class='n'>s</span> <span class='o'>=</span> <span class='nb'>next</span><span class='p'>(</span><span class='n'>d_male</span><span class='p'>)</span>
+            <span class='n'>speaker_id</span> <span class='o'>=</span> <span class='n'>s</span><span class='p'>[</span><span class='o'>-</span><span class='mi'>3</span><span class='p'>]</span>
+            <span class='k'>if</span> <span class='n'>speaker_id</span> <span class='ow'>not</span> <span class='ow'>in</span> <span class='n'>male_unique</span><span class='p'>:</span>
+                <span class='n'>male_unique</span><span class='p'>[</span><span class='n'>speaker_id</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>s</span>
+
+        <span class='n'>female_unique</span> <span class='o'>=</span> <span class='p'>{}</span>
+        <span class='k'>while</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>female_unique</span><span class='p'>)</span> <span class='o'>&lt;</span> <span class='n'>samples</span> <span class='o'>//</span> <span class='mi'>2</span><span class='p'>:</span>
+            <span class='n'>s</span> <span class='o'>=</span> <span class='nb'>next</span><span class='p'>(</span><span class='n'>d_female</span><span class='p'>)</span>
+            <span class='n'>speaker_id</span> <span class='o'>=</span> <span class='n'>s</span><span class='p'>[</span><span class='o'>-</span><span class='mi'>3</span><span class='p'>]</span>
+            <span class='k'>if</span> <span class='n'>speaker_id</span> <span class='ow'>not</span> <span class='ow'>in</span> <span class='n'>female_unique</span><span class='p'>:</span>
+                <span class='n'>female_unique</span><span class='p'>[</span><span class='n'>speaker_id</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>s</span>
+
+        <span class='n'>unique</span> <span class='o'>=</span> <span class='p'>(</span><span class='o'>*</span><span class='n'>male_unique</span><span class='o'>.</span><span class='n'>values</span><span class='p'>(),</span> <span class='o'>*</span><span class='n'>female_unique</span><span class='o'>.</span><span class='n'>values</span><span class='p'>())</span>
+
+        <span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span> <span class='o'>=</span> <span class='nb'>zip</span><span class='p'>(</span><span class='o'>*</span><span class='p'>[(</span><span class='n'>v</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span><span class='o'>.</span><span class='n'>numpy</span><span class='p'>(),</span> <span class='n'>v</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>])</span> <span class='k'>for</span> <span class='n'>_</span><span class='p'>,</span> <span class='n'>v</span> <span class='ow'>in</span> <span class='nb'>zip</span><span class='p'>(</span><span class='nb'>range</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>),</span> <span class='n'>unique</span><span class='p'>)])</span>
+        <span class='k'>assert</span> <span class='nb'>all</span><span class='p'>([</span><span class='n'>sr</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span> <span class='o'>==</span> <span class='n'>s</span> <span class='k'>for</span> <span class='n'>s</span> <span class='ow'>in</span> <span class='n'>sr</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:]])</span>
+        <span class='n'>sr</span> <span class='o'>=</span> <span class='n'>sr</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span>
+
+        <span class='n'>noise_welch</span> <span class='o'>=</span> <span class='n'>generate_speech_shaped_noise</span><span class='p'>(</span>
+            <span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>,</span> <span class='n'>export_path</span><span class='o'>=</span><span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='s2'>&quot;ssn_welch.wav&quot;</span>
+        <span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+        <span class='n'>noise_fft</span> <span class='o'>=</span> <span class='n'>generate_speech_shaped_noise</span><span class='p'>(</span>
+            <span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>,</span> <span class='n'>export_path</span><span class='o'>=</span><span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='s2'>&quot;ssn_fft.wav&quot;</span>
+        <span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+        <span class='n'>files</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>concatenate</span><span class='p'>(</span><span class='n'>files</span><span class='p'>,</span> <span class='o'>-</span><span class='mi'>1</span><span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+        <span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>save</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='s2'>&quot;ssn_signal.wav&quot;</span><span class='p'>),</span> <span class='n'>to_tensor</span><span class='p'>(</span><span class='n'>files</span><span class='p'>),</span> <span class='n'>sr</span><span class='p'>)</span>
+
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;signal&quot;</span><span class='p'>)</span>
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>noise_welch</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;noise_welch&quot;</span><span class='p'>)</span>
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>noise_fft</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;noise_fft&quot;</span><span class='p'>)</span>
+
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>legend</span><span class='p'>()</span>
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>show</span><span class='p'>()</span>
+
+    <span class='c1'># reald()</span>
+    <span class='c1'># synth()</span>
+    <span class='n'>distinct_real</span><span class='p'>()</span>
+</pre>
+            </div>
+
+          </div>
+
+        </div>
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diff --git a/docs/github/html/_modules/modulation/regression/linear_predictive_coding.html b/docs/github/html/_modules/modulation/regression/linear_predictive_coding.html
index 0011c53..e93628b 100644
--- a/docs/github/html/_modules/modulation/regression/linear_predictive_coding.html
+++ b/docs/github/html/_modules/modulation/regression/linear_predictive_coding.html
@@ -1,169 +1,163 @@
-
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     <title>modulation.regression.linear_predictive_coding &#8212; modulation 0.0.1 documentation</title>
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-            
-  <h1>Source code for modulation.regression.linear_predictive_coding</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 24-02-2021</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-
-
-<div class="viewcode-block" id="lpc_simple"><a class="viewcode-back" href="../../../generated/modulation.regression.linear_predictive_coding.lpc_simple.html#modulation.regression.linear_predictive_coding.lpc_simple">[docs]</a><span class="k">def</span> <span class="nf">lpc_simple</span><span class="p">(</span><span class="n">y</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">m</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-  <span class="s2">&quot;Return m linear predictive coefficients for sequence y using Levinson-Durbin prediction algorithm&quot;</span>
-
-  <span class="c1"># step 1: compute autoregression coefficients R_0, ..., R_m</span>
-  <span class="n">r</span> <span class="o">=</span> <span class="p">[</span><span class="n">y</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">y</span><span class="p">)]</span>
-  <span class="k">if</span> <span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-    <span class="k">return</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">m</span> <span class="o">-</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-  <span class="k">else</span><span class="p">:</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">m</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-      <span class="n">r</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">:]</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">y</span><span class="p">[:</span><span class="o">-</span><span class="n">i</span><span class="p">])</span>
-      <span class="n">r</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">r</span><span class="p">)</span>
-    <span class="n">r</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">r</span><span class="p">)</span>
-
-    <span class="c1"># step 2:</span>
-    <span class="n">a</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
-    <span class="n">e</span> <span class="o">=</span> <span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">m</span><span class="p">):</span>
-      <span class="k">if</span> <span class="n">e</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">e</span> <span class="o">=</span> <span class="mf">10e-17</span>
-      <span class="n">alpha</span> <span class="o">=</span> <span class="o">-</span><span class="n">a</span><span class="p">[:</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:</span> <span class="mi">0</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="n">e</span>
-      <span class="n">a</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
-      <span class="n">a</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">alpha</span> <span class="o">*</span> <span class="n">a</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-      <span class="n">e</span> <span class="o">*=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span> <span class="o">**</span> <span class="mi">2</span>
-
-    <span class="k">return</span> <span class="n">a</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-
-  <span class="k">def</span> <span class="nf">asidj</span><span class="p">():</span>
-    <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-    <span class="kn">import</span> <span class="nn">librosa</span>
-    <span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span>
-
-    <span class="kn">import</span> <span class="nn">scipy</span>
-
-    <span class="n">y</span><span class="p">,</span> <span class="n">sr</span> <span class="o">=</span> <span class="n">librosa</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">librosa</span><span class="o">.</span><span class="n">ex</span><span class="p">(</span><span class="s2">&quot;trumpet&quot;</span><span class="p">),</span> <span class="n">duration</span> <span class="o">=</span> <span class="mf">0.020</span><span class="p">)</span>
-    <span class="n">a</span> <span class="o">=</span> <span class="n">librosa</span><span class="o">.</span><span class="n">lpc</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-    <span class="n">b</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">hstack</span><span class="p">([[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">:]])</span>
-    <span class="n">y_hat</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">signal</span><span class="o">.</span><span class="n">lfilter</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">y</span><span class="p">)</span>
-    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y_hat</span><span class="p">,</span> <span class="n">linestyle</span> <span class="o">=</span> <span class="s2">&quot;--&quot;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">&quot;y&quot;</span><span class="p">,</span> <span class="s2">&quot;y_hat&quot;</span><span class="p">])</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;LP Model Forward Prediction&quot;</span><span class="p">)</span>
-</pre></div>
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+
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+
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+        <div class='bodywrapper'>
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+
+            <h1>Source code for modulation.regression.linear_predictive_coding</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 24-02-2021</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
 
+<span class='kn'>import</span> <span class='nn'>numpy</span>
 
 
+<div class='viewcode-block' id='lpc_simple'><a class='viewcode-back' href='../../../generated/modulation.regression.linear_predictive_coding.lpc_simple.html#modulation.regression.linear_predictive_coding.lpc_simple'>[docs]</a><span class='k'>def</span> <span class='nf'>lpc_simple</span><span class='p'>(</span><span class='n'>y</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='n'>m</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+  <span class='s2'>&quot;Return m linear predictive coefficients for sequence y using Levinson-Durbin prediction algorithm&quot;</span>
 
+  <span class='c1'># step 1: compute autoregression coefficients R_0, ..., R_m</span>
+  <span class='n'>r</span> <span class='o'>=</span> <span class='p'>[</span><span class='n'>y</span><span class='o'>.</span><span class='n'>dot</span><span class='p'>(</span><span class='n'>y</span><span class='p'>)]</span>
+  <span class='k'>if</span> <span class='n'>r</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span> <span class='o'>==</span> <span class='mi'>0</span><span class='p'>:</span>
+    <span class='k'>return</span> <span class='p'>[</span><span class='mi'>1</span><span class='p'>]</span> <span class='o'>+</span> <span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span> <span class='o'>*</span> <span class='p'>(</span><span class='n'>m</span> <span class='o'>-</span> <span class='mi'>2</span><span class='p'>)</span> <span class='o'>+</span> <span class='p'>[</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>]</span>
+  <span class='k'>else</span><span class='p'>:</span>
+    <span class='k'>for</span> <span class='n'>i</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>,</span> <span class='n'>m</span> <span class='o'>+</span> <span class='mi'>1</span><span class='p'>):</span>
+      <span class='n'>r</span> <span class='o'>=</span> <span class='n'>y</span><span class='p'>[</span><span class='n'>i</span><span class='p'>:]</span><span class='o'>.</span><span class='n'>dot</span><span class='p'>(</span><span class='n'>y</span><span class='p'>[:</span><span class='o'>-</span><span class='n'>i</span><span class='p'>])</span>
+      <span class='n'>r</span><span class='o'>.</span><span class='n'>append</span><span class='p'>(</span><span class='n'>r</span><span class='p'>)</span>
+    <span class='n'>r</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>(</span><span class='n'>r</span><span class='p'>)</span>
 
+    <span class='c1'># step 2:</span>
+    <span class='n'>a</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>([</span><span class='mi'>1</span><span class='p'>,</span> <span class='o'>-</span><span class='n'>r</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>]</span> <span class='o'>/</span> <span class='n'>r</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]])</span>
+    <span class='n'>e</span> <span class='o'>=</span> <span class='n'>r</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span> <span class='o'>+</span> <span class='n'>r</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>]</span> <span class='o'>*</span> <span class='n'>a</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>]</span>
+    <span class='k'>for</span> <span class='n'>k</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>,</span> <span class='n'>m</span><span class='p'>):</span>
+      <span class='k'>if</span> <span class='n'>e</span> <span class='o'>==</span> <span class='mi'>0</span><span class='p'>:</span>
+        <span class='n'>e</span> <span class='o'>=</span> <span class='mf'>10e-17</span>
+      <span class='n'>alpha</span> <span class='o'>=</span> <span class='o'>-</span><span class='n'>a</span><span class='p'>[:</span> <span class='n'>k</span> <span class='o'>+</span> <span class='mi'>1</span><span class='p'>]</span><span class='o'>.</span><span class='n'>dot</span><span class='p'>(</span><span class='n'>r</span><span class='p'>[</span><span class='n'>k</span> <span class='o'>+</span> <span class='mi'>1</span><span class='p'>:</span> <span class='mi'>0</span><span class='p'>:</span> <span class='o'>-</span><span class='mi'>1</span><span class='p'>])</span> <span class='o'>/</span> <span class='n'>e</span>
+      <span class='n'>a</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>hstack</span><span class='p'>([</span><span class='n'>a</span><span class='p'>,</span> <span class='mi'>0</span><span class='p'>])</span>
+      <span class='n'>a</span> <span class='o'>=</span> <span class='n'>a</span> <span class='o'>+</span> <span class='n'>alpha</span> <span class='o'>*</span> <span class='n'>a</span><span class='p'>[::</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>]</span>
+      <span class='n'>e</span> <span class='o'>*=</span> <span class='mi'>1</span> <span class='o'>-</span> <span class='n'>alpha</span> <span class='o'>**</span> <span class='mi'>2</span>
 
+    <span class='k'>return</span> <span class='n'>a</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+
+  <span class='k'>def</span> <span class='nf'>asidj</span><span class='p'>():</span>
+    <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+    <span class='kn'>import</span> <span class='nn'>librosa</span>
+    <span class='kn'>from</span> <span class='nn'>matplotlib</span> <span class='kn'>import</span> <span class='n'>pyplot</span>
+
+    <span class='kn'>import</span> <span class='nn'>scipy</span>
+
+    <span class='n'>y</span><span class='p'>,</span> <span class='n'>sr</span> <span class='o'>=</span> <span class='n'>librosa</span><span class='o'>.</span><span class='n'>load</span><span class='p'>(</span><span class='n'>librosa</span><span class='o'>.</span><span class='n'>ex</span><span class='p'>(</span><span class='s2'>&quot;trumpet&quot;</span><span class='p'>),</span> <span class='n'>duration</span> <span class='o'>=</span> <span class='mf'>0.020</span><span class='p'>)</span>
+    <span class='n'>a</span> <span class='o'>=</span> <span class='n'>librosa</span><span class='o'>.</span><span class='n'>lpc</span><span class='p'>(</span><span class='n'>y</span><span class='p'>,</span> <span class='mi'>2</span><span class='p'>)</span>
+    <span class='n'>b</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>hstack</span><span class='p'>([[</span><span class='mi'>0</span><span class='p'>],</span> <span class='o'>-</span><span class='mi'>1</span> <span class='o'>*</span> <span class='n'>a</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:]])</span>
+    <span class='n'>y_hat</span> <span class='o'>=</span> <span class='n'>scipy</span><span class='o'>.</span><span class='n'>signal</span><span class='o'>.</span><span class='n'>lfilter</span><span class='p'>(</span><span class='n'>b</span><span class='p'>,</span> <span class='p'>[</span><span class='mi'>1</span><span class='p'>],</span> <span class='n'>y</span><span class='p'>)</span>
+    <span class='n'>fig</span><span class='p'>,</span> <span class='n'>ax</span> <span class='o'>=</span> <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>subplots</span><span class='p'>()</span>
+    <span class='n'>ax</span><span class='o'>.</span><span class='n'>plot</span><span class='p'>(</span><span class='n'>y</span><span class='p'>)</span>
+    <span class='n'>ax</span><span class='o'>.</span><span class='n'>plot</span><span class='p'>(</span><span class='n'>y_hat</span><span class='p'>,</span> <span class='n'>linestyle</span> <span class='o'>=</span> <span class='s2'>&quot;--&quot;</span><span class='p'>)</span>
+    <span class='n'>ax</span><span class='o'>.</span><span class='n'>legend</span><span class='p'>([</span><span class='s2'>&quot;y&quot;</span><span class='p'>,</span> <span class='s2'>&quot;y_hat&quot;</span><span class='p'>])</span>
+    <span class='n'>ax</span><span class='o'>.</span><span class='n'>set_title</span><span class='p'>(</span><span class='s2'>&quot;LP Model Forward Prediction&quot;</span><span class='p'>)</span>
+</pre>
+            </div>
+
+          </div>
+
+        </div>
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+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
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+          <ul>
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diff --git a/docs/github/html/_modules/modulation/regression/spectral_denoise.html b/docs/github/html/_modules/modulation/regression/spectral_denoise.html
index 38eb5e6..10c225f 100644
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+++ b/docs/github/html/_modules/modulation/regression/spectral_denoise.html
@@ -1,141 +1,135 @@
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     <title>modulation.regression.spectral_denoise &#8212; modulation 0.0.1 documentation</title>
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-            
-  <h1>Source code for modulation.regression.spectral_denoise</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 09/04/2020</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-
-
-<div class="viewcode-block" id="fft_denoise"><a class="viewcode-back" href="../../../generated/modulation.regression.spectral_denoise.fft_denoise.html#modulation.regression.spectral_denoise.fft_denoise">[docs]</a><span class="k">def</span> <span class="nf">fft_denoise</span><span class="p">(</span>
-    <span class="n">noisy_signal</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">num_time_steps</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">cutoff_threshold</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">100</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-  <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-
-  <span class="n">noisy_signal_f_coef</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span>
-      <span class="n">noisy_signal</span><span class="p">,</span> <span class="n">num_time_steps</span>
-      <span class="p">)</span>  <span class="c1"># Frequency coefficients in frequency space</span>
-  <span class="n">power_spectral_density</span> <span class="o">=</span> <span class="p">(</span>
-      <span class="n">noisy_signal_f_coef</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">noisy_signal_f_coef</span><span class="p">)</span> <span class="o">/</span> <span class="n">num_time_steps</span>
-  <span class="p">)</span>  <span class="c1"># PSD</span>
-
-  <span class="n">indices</span> <span class="o">=</span> <span class="n">power_spectral_density</span> <span class="o">&gt;</span> <span class="n">cutoff_threshold</span>  <span class="c1"># 1 for true, 0 for false</span>
-
-  <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">ifft</span><span class="p">(</span>
-      <span class="n">noisy_signal_f_coef</span> <span class="o">*</span> <span class="n">indices</span>
-      <span class="p">)</span>  <span class="c1"># Inverse to signal space, cleaned</span></div>
-</pre></div>
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+          <div class='body' role='main'>
 
+            <h1>Source code for modulation.regression.spectral_denoise</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
 
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
 
+<span class='s2'>           Created on 09/04/2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
 
+<span class='kn'>import</span> <span class='nn'>numpy</span>
 
 
+<div class='viewcode-block' id='fft_denoise'><a class='viewcode-back' href='../../../generated/modulation.regression.spectral_denoise.fft_denoise.html#modulation.regression.spectral_denoise.fft_denoise'>[docs]</a><span class='k'>def</span> <span class='nf'>fft_denoise</span><span class='p'>(</span>
+    <span class='n'>noisy_signal</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='n'>num_time_steps</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span> <span class='n'>cutoff_threshold</span><span class='p'>:</span> <span class='nb'>float</span> <span class='o'>=</span> <span class='mi'>100</span>
+    <span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+  <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+
+  <span class='n'>noisy_signal_f_coef</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>fft</span><span class='o'>.</span><span class='n'>fft</span><span class='p'>(</span>
+      <span class='n'>noisy_signal</span><span class='p'>,</span> <span class='n'>num_time_steps</span>
+      <span class='p'>)</span>  <span class='c1'># Frequency coefficients in frequency space</span>
+  <span class='n'>power_spectral_density</span> <span class='o'>=</span> <span class='p'>(</span>
+      <span class='n'>noisy_signal_f_coef</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>conj</span><span class='p'>(</span><span class='n'>noisy_signal_f_coef</span><span class='p'>)</span> <span class='o'>/</span> <span class='n'>num_time_steps</span>
+  <span class='p'>)</span>  <span class='c1'># PSD</span>
+
+  <span class='n'>indices</span> <span class='o'>=</span> <span class='n'>power_spectral_density</span> <span class='o'>&gt;</span> <span class='n'>cutoff_threshold</span>  <span class='c1'># 1 for true, 0 for false</span>
+
+  <span class='k'>return</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>fft</span><span class='o'>.</span><span class='n'>ifft</span><span class='p'>(</span>
+      <span class='n'>noisy_signal_f_coef</span> <span class='o'>*</span> <span class='n'>indices</span>
+      <span class='p'>)</span>  <span class='c1'># Inverse to signal space, cleaned</span></div>
+</pre>
+            </div>
+
+          </div>
+
+        </div>
+      </div>
+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <p class='logo'><a href='../../../index.html'>
+            <img alt='Logo' class='logo' src='../../../_static/mod.svg'/>
+          </a></p>
+          <h1 class='logo'><a href='../../../index.html'>modulation</a></h1>
+
+
+          <h3>Navigation</h3>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../generated/modulation.html'>modulation</a></li>
+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../../../index.html'>Documentation overview</a>
+                <ul>
+                  <li><a href='../../index.html'>Module code</a>
+                    <ul>
+                      <li><a href='../../modulation.html'>modulation</a>
+                        <ul>
+                        </ul>
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diff --git a/docs/github/html/_modules/modulation/salience/colorise_waveform.html b/docs/github/html/_modules/modulation/salience/colorise_waveform.html
new file mode 100644
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--- /dev/null
+++ b/docs/github/html/_modules/modulation/salience/colorise_waveform.html
@@ -0,0 +1,223 @@
+<!DOCTYPE html>
+
+<html lang='en'>
+  <head>
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
+    <title>modulation.salience.colorise_waveform &#8212; modulation 0.0.1 documentation</title>
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+
+            <h1>Source code for modulation.salience.colorise_waveform</h1>
+            <div class='highlight'><pre>
+<span></span><span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Optional</span><span class='p'>,</span> <span class='n'>Sequence</span>
+
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>from</span> <span class='nn'>matplotlib</span> <span class='kn'>import</span> <span class='n'>pyplot</span>
+<span class='kn'>from</span> <span class='nn'>matplotlib.collections</span> <span class='kn'>import</span> <span class='n'>LineCollection</span>
+<span class='kn'>from</span> <span class='nn'>matplotlib.colors</span> <span class='kn'>import</span> <span class='n'>BoundaryNorm</span><span class='p'>,</span> <span class='n'>ListedColormap</span>
+<span class='kn'>from</span> <span class='nn'>warg</span> <span class='kn'>import</span> <span class='n'>Number</span>
+
+
+<div class='viewcode-block' id='colorise_waveform_frequency_by_components'><a class='viewcode-back' href='../../../generated/modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components.html#modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components'>[docs]</a><span class='k'>def</span> <span class='nf'>colorise_waveform_frequency_by_components</span><span class='p'>():</span>
+  <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>  &quot;&quot;&quot;</span>
+  <span class='k'>pass</span></div>
+
+
+<div class='viewcode-block' id='colorise_waveform_by_salience'><a class='viewcode-back' href='../../../generated/modulation.salience.colorise_waveform.colorise_waveform_by_salience.html#modulation.salience.colorise_waveform.colorise_waveform_by_salience'>[docs]</a><span class='k'>def</span> <span class='nf'>colorise_waveform_by_salience</span><span class='p'>():</span>
+  <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>  &quot;&quot;&quot;</span>
+  <span class='k'>pass</span></div>
+
+
+<div class='viewcode-block' id='color_plot'><a class='viewcode-back' href='../../../generated/modulation.salience.colorise_waveform.color_plot.html#modulation.salience.colorise_waveform.color_plot'>[docs]</a><span class='k'>def</span> <span class='nf'>color_plot</span><span class='p'>(</span>
+    <span class='n'>xy</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span>
+    <span class='n'>colors</span><span class='p'>:</span> <span class='n'>Optional</span><span class='p'>[</span><span class='n'>Sequence</span><span class='p'>[</span><span class='n'>Number</span><span class='p'>]]</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>,</span>
+    <span class='n'>cmap</span> <span class='o'>=</span> <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>cm</span><span class='o'>.</span><span class='n'>gist_rainbow</span><span class='p'>,</span>  <span class='c1'># cmap=pyplot.cm.gist_ncar</span>
+    <span class='p'>)</span> <span class='o'>-&gt;</span> <span class='kc'>None</span><span class='p'>:</span>
+  <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>  :param xy:</span>
+<span class='sd'>  :type xy:</span>
+<span class='sd'>  :param colors:</span>
+<span class='sd'>  :type colors:</span>
+<span class='sd'>  :param cmap:</span>
+<span class='sd'>  :type cmap:</span>
+<span class='sd'>  &quot;&quot;&quot;</span>
+  <span class='n'>fig</span><span class='p'>,</span> <span class='n'>ax</span> <span class='o'>=</span> <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>subplots</span><span class='p'>()</span>
+
+  <span class='c1'># Reshape things so that we have a sequence of:</span>
+  <span class='c1'># [[(x0,y0),(x1,y1)],[(x0,y0),(x1,y1)],...]</span>
+  <span class='n'>xy</span> <span class='o'>=</span> <span class='n'>xy</span><span class='o'>.</span><span class='n'>reshape</span><span class='p'>(</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>,</span> <span class='mi'>1</span><span class='p'>,</span> <span class='mi'>2</span><span class='p'>)</span>
+  <span class='n'>segments</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>hstack</span><span class='p'>([</span><span class='n'>xy</span><span class='p'>[:</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>],</span> <span class='n'>xy</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:]])</span>
+
+  <span class='n'>coll</span> <span class='o'>=</span> <span class='n'>LineCollection</span><span class='p'>(</span><span class='n'>segments</span><span class='p'>,</span> <span class='n'>cmap</span> <span class='o'>=</span> <span class='n'>cmap</span><span class='p'>)</span>
+
+  <span class='k'>if</span> <span class='n'>colors</span> <span class='ow'>is</span> <span class='kc'>None</span><span class='p'>:</span>
+    <span class='n'>colors</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>arange</span><span class='p'>(</span><span class='n'>xy</span><span class='o'>.</span><span class='n'>shape</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>])</span>
+
+  <span class='n'>colors</span> <span class='o'>=</span> <span class='p'>(</span><span class='mi'>1</span> <span class='o'>/</span> <span class='n'>xy</span><span class='o'>.</span><span class='n'>shape</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>])</span> <span class='o'>*</span> <span class='n'>colors</span>
+  <span class='c1'># cyclic color = numpy.sin(colors)</span>
+  <span class='n'>coll</span><span class='o'>.</span><span class='n'>set_array</span><span class='p'>(</span><span class='n'>colors</span><span class='p'>)</span>
+
+  <span class='n'>ax</span><span class='o'>.</span><span class='n'>add_collection</span><span class='p'>(</span><span class='n'>coll</span><span class='p'>)</span>
+  <span class='n'>ax</span><span class='o'>.</span><span class='n'>autoscale_view</span><span class='p'>()</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+
+  <span class='k'>def</span> <span class='nf'>main</span><span class='p'>():</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>xy</span> <span class='o'>=</span> <span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>random</span><span class='o'>.</span><span class='n'>random</span><span class='p'>((</span><span class='mi'>1000</span><span class='p'>,</span> <span class='mi'>2</span><span class='p'>))</span> <span class='o'>-</span> <span class='mf'>0.5</span><span class='p'>)</span><span class='o'>.</span><span class='n'>cumsum</span><span class='p'>(</span><span class='n'>axis</span> <span class='o'>=</span> <span class='mi'>0</span><span class='p'>)</span>
+
+    <span class='n'>color_plot</span><span class='p'>(</span><span class='n'>xy</span><span class='p'>)</span>
+    <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>show</span><span class='p'>()</span>
+
+
+  <span class='k'>def</span> <span class='nf'>main2</span><span class='p'>():</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    Color parts of a line based on its properties, e.g., slope.</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+    <span class='n'>x</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>linspace</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='mi'>3</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>pi</span><span class='p'>,</span> <span class='mi'>500</span><span class='p'>)</span>
+    <span class='n'>y</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sin</span><span class='p'>(</span><span class='n'>x</span><span class='p'>)</span>
+    <span class='n'>z</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>cos</span><span class='p'>(</span><span class='mf'>0.5</span> <span class='o'>*</span> <span class='p'>(</span><span class='n'>x</span><span class='p'>[:</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>]</span> <span class='o'>+</span> <span class='n'>x</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:]))</span>  <span class='c1'># first derivative</span>
+
+    <span class='c1'># Create a colormap for red, green and blue and a norm to color</span>
+    <span class='c1'># f&#39; &lt; -0.5 red, f&#39; &gt; 0.5 blue, and the rest green</span>
+    <span class='n'>cmap</span> <span class='o'>=</span> <span class='n'>ListedColormap</span><span class='p'>([</span><span class='s2'>&quot;r&quot;</span><span class='p'>,</span> <span class='s2'>&quot;g&quot;</span><span class='p'>,</span> <span class='s2'>&quot;b&quot;</span><span class='p'>])</span>
+    <span class='n'>norm</span> <span class='o'>=</span> <span class='n'>BoundaryNorm</span><span class='p'>([</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>,</span> <span class='o'>-</span><span class='mf'>0.5</span><span class='p'>,</span> <span class='mf'>0.5</span><span class='p'>,</span> <span class='mi'>1</span><span class='p'>],</span> <span class='n'>cmap</span><span class='o'>.</span><span class='n'>N</span><span class='p'>)</span>
+
+    <span class='c1'># Create a set of line segments so that we can color them individually</span>
+    <span class='c1'># This creates the points as a N x 1 x 2 array so that we can stack points</span>
+    <span class='c1'># together easily to get the segments. The segments array for line collection</span>
+    <span class='c1'># needs to be numlines x points per line x 2 (x and y)</span>
+    <span class='n'>points</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>([</span><span class='n'>x</span><span class='p'>,</span> <span class='n'>y</span><span class='p'>])</span><span class='o'>.</span><span class='n'>T</span><span class='o'>.</span><span class='n'>reshape</span><span class='p'>(</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>,</span> <span class='mi'>1</span><span class='p'>,</span> <span class='mi'>2</span><span class='p'>)</span>
+    <span class='n'>segments</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>concatenate</span><span class='p'>([</span><span class='n'>points</span><span class='p'>[:</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>],</span> <span class='n'>points</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:]],</span> <span class='n'>axis</span> <span class='o'>=</span> <span class='mi'>1</span><span class='p'>)</span>
+
+    <span class='c1'># Create the line collection object, setting the colormapping parameters.</span>
+    <span class='c1'># Have to set the actual values used for colormapping separately.</span>
+    <span class='n'>lc</span> <span class='o'>=</span> <span class='n'>LineCollection</span><span class='p'>(</span><span class='n'>segments</span><span class='p'>,</span> <span class='n'>cmap</span> <span class='o'>=</span> <span class='n'>cmap</span><span class='p'>,</span> <span class='n'>norm</span> <span class='o'>=</span> <span class='n'>norm</span><span class='p'>)</span>
+    <span class='n'>lc</span><span class='o'>.</span><span class='n'>set_array</span><span class='p'>(</span><span class='n'>z</span><span class='p'>)</span>
+    <span class='n'>lc</span><span class='o'>.</span><span class='n'>set_linewidth</span><span class='p'>(</span><span class='mi'>3</span><span class='p'>)</span>
+
+    <span class='n'>fig1</span> <span class='o'>=</span> <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>figure</span><span class='p'>()</span>
+    <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>gca</span><span class='p'>()</span><span class='o'>.</span><span class='n'>add_collection</span><span class='p'>(</span><span class='n'>lc</span><span class='p'>)</span>
+    <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>xlim</span><span class='p'>(</span><span class='n'>x</span><span class='o'>.</span><span class='n'>min</span><span class='p'>(),</span> <span class='n'>x</span><span class='o'>.</span><span class='n'>max</span><span class='p'>())</span>
+    <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>ylim</span><span class='p'>(</span><span class='o'>-</span><span class='mf'>1.1</span><span class='p'>,</span> <span class='mf'>1.1</span><span class='p'>)</span>
+
+    <span class='c1'># Now do a second plot coloring the curve using a continuous colormap</span>
+    <span class='n'>t</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>linspace</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='mi'>10</span><span class='p'>,</span> <span class='mi'>200</span><span class='p'>)</span>
+    <span class='n'>x</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>cos</span><span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>pi</span> <span class='o'>*</span> <span class='n'>t</span><span class='p'>)</span>
+    <span class='n'>y</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sin</span><span class='p'>(</span><span class='n'>t</span><span class='p'>)</span>
+    <span class='n'>points</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>([</span><span class='n'>x</span><span class='p'>,</span> <span class='n'>y</span><span class='p'>])</span><span class='o'>.</span><span class='n'>T</span><span class='o'>.</span><span class='n'>reshape</span><span class='p'>(</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>,</span> <span class='mi'>1</span><span class='p'>,</span> <span class='mi'>2</span><span class='p'>)</span>
+    <span class='n'>segments</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>concatenate</span><span class='p'>([</span><span class='n'>points</span><span class='p'>[:</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>],</span> <span class='n'>points</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:]],</span> <span class='n'>axis</span> <span class='o'>=</span> <span class='mi'>1</span><span class='p'>)</span>
+
+    <span class='n'>lc</span> <span class='o'>=</span> <span class='n'>LineCollection</span><span class='p'>(</span><span class='n'>segments</span><span class='p'>,</span> <span class='n'>cmap</span> <span class='o'>=</span> <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>get_cmap</span><span class='p'>(</span><span class='s2'>&quot;copper&quot;</span><span class='p'>),</span> <span class='n'>norm</span> <span class='o'>=</span> <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>Normalize</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='mi'>10</span><span class='p'>))</span>
+    <span class='n'>lc</span><span class='o'>.</span><span class='n'>set_array</span><span class='p'>(</span><span class='n'>t</span><span class='p'>)</span>
+    <span class='n'>lc</span><span class='o'>.</span><span class='n'>set_linewidth</span><span class='p'>(</span><span class='mi'>3</span><span class='p'>)</span>
+
+    <span class='n'>fig2</span> <span class='o'>=</span> <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>figure</span><span class='p'>()</span>
+    <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>gca</span><span class='p'>()</span><span class='o'>.</span><span class='n'>add_collection</span><span class='p'>(</span><span class='n'>lc</span><span class='p'>)</span>
+    <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>xlim</span><span class='p'>(</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>,</span> <span class='mi'>1</span><span class='p'>)</span>
+    <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>ylim</span><span class='p'>(</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>,</span> <span class='mi'>1</span><span class='p'>)</span>
+    <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>show</span><span class='p'>()</span>
+
+
+  <span class='n'>main</span><span class='p'>()</span>
+</pre>
+            </div>
+
+          </div>
+
+        </div>
+      </div>
+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <h1 class='logo'><a href='../../../index.html'>modulation</a></h1>
+
+
+          <h3>Navigation</h3>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../generated/modulation.html'>modulation</a></li>
+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../../../index.html'>Documentation overview</a>
+                <ul>
+                  <li><a href='../../index.html'>Module code</a>
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+                      <li><a href='../../modulation.html'>modulation</a>
+                        <ul>
+                        </ul>
+                      </li>
+                    </ul>
+                  </li>
+                </ul>
+              </li>
+            </ul>
+          </div>
+          <div id='searchbox' role='search' style='display: none'>
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+            <div class='searchformwrapper'>
+              <form action='../../../search.html' class='search' method='get'>
+                <input aria-labelledby='searchlabel' autocapitalize='off' autocomplete='off' autocorrect='off' name='q' spellcheck='false' type='text'/>
+                <input type='submit' value='Go'/>
+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
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+      <div class='clearer'></div>
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+
+      |
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
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+
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+</html>
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diff --git a/docs/github/html/_modules/modulation/signal_utilities/audio_utilities/filtering.html b/docs/github/html/_modules/modulation/signal_utilities/audio_utilities/filtering.html
index ec97638..a4b0797 100644
--- a/docs/github/html/_modules/modulation/signal_utilities/audio_utilities/filtering.html
+++ b/docs/github/html/_modules/modulation/signal_utilities/audio_utilities/filtering.html
@@ -1,230 +1,224 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
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-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
     <title>modulation.signal_utilities.audio_utilities.filtering &#8212; modulation 0.0.1 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
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-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          
-
-          <div class="body" role="main">
-            
-  <h1>Source code for modulation.signal_utilities.audio_utilities.filtering</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 11-01-2021</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">scipy.signal</span> <span class="kn">import</span> <span class="n">hilbert</span><span class="p">,</span> <span class="n">lfilter</span>
-<span class="kn">from</span> <span class="nn">warg</span> <span class="kn">import</span> <span class="n">next_pow_2</span>
-
-
-<div class="viewcode-block" id="mel_scale"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.audio_utilities.filtering.mel_scale.html#modulation.signal_utilities.audio_utilities.filtering.mel_scale">[docs]</a><span class="k">def</span> <span class="nf">mel_scale</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param x:</span>
-<span class="sd">    :type x:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="mf">2595.0</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="mf">1.0</span> <span class="o">+</span> <span class="n">x</span> <span class="o">/</span> <span class="mf">700.0</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="in_mel_scale"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.audio_utilities.filtering.in_mel_scale.html#modulation.signal_utilities.audio_utilities.filtering.in_mel_scale">[docs]</a><span class="k">def</span> <span class="nf">in_mel_scale</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param x:</span>
-<span class="sd">    :type x:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="mi">700</span> <span class="o">*</span> <span class="p">(</span><span class="mi">10</span> <span class="o">**</span> <span class="p">(</span><span class="n">x</span> <span class="o">/</span> <span class="mf">2595.0</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="pre_emphasise"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.audio_utilities.filtering.pre_emphasise.html#modulation.signal_utilities.audio_utilities.filtering.pre_emphasise">[docs]</a><span class="k">def</span> <span class="nf">pre_emphasise</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">coeff</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.97</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param x:</span>
-<span class="sd">    :type x:</span>
-<span class="sd">    :param coeff:</span>
-<span class="sd">    :type coeff:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">lfilter</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="n">coeff</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">x</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="de_emphasise"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.audio_utilities.filtering.de_emphasise.html#modulation.signal_utilities.audio_utilities.filtering.de_emphasise">[docs]</a><span class="k">def</span> <span class="nf">de_emphasise</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">coeff</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.97</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param x:</span>
-<span class="sd">    :type x:</span>
-<span class="sd">    :param coeff:</span>
-<span class="sd">    :type coeff:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">lfilter</span><span class="p">([</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="n">coeff</span><span class="p">],</span> <span class="n">x</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="pre_emphasise_torch"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.audio_utilities.filtering.pre_emphasise_torch.html#modulation.signal_utilities.audio_utilities.filtering.pre_emphasise_torch">[docs]</a><span class="k">def</span> <span class="nf">pre_emphasise_torch</span><span class="p">(</span><span class="n">signal</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">coeff</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.97</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Pre-emphasis on the input signal</span>
-<span class="sd">    :param signal: (time,)</span>
-<span class="sd">    :param coeff:</span>
-<span class="sd">    :return: (time,)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">signal</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">],</span> <span class="n">signal</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="n">coeff</span> <span class="o">*</span> <span class="n">signal</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span></div>
-
-
-<div class="viewcode-block" id="fft_frequencies"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.audio_utilities.filtering.fft_frequencies.html#modulation.signal_utilities.audio_utilities.filtering.fft_frequencies">[docs]</a><span class="k">def</span> <span class="nf">fft_frequencies</span><span class="p">(</span><span class="n">sr</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">16000</span><span class="p">,</span> <span class="n">n_fft</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">512</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param sr:</span>
-<span class="sd">    :type sr:</span>
-<span class="sd">    :param n_fft:</span>
-<span class="sd">    :type n_fft:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">sr</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">n_fft</span> <span class="o">//</span> <span class="mi">2</span><span class="p">),</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="hilbert_envelope"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.audio_utilities.filtering.hilbert_envelope.html#modulation.signal_utilities.audio_utilities.filtering.hilbert_envelope">[docs]</a><span class="k">def</span> <span class="nf">hilbert_envelope</span><span class="p">(</span><span class="n">signal</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">n_fft</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;Calculates the Hilbert envelope of a signal.</span>
-
-<span class="sd">    :param signal: The signal to calculate the Hilbert envelope of.</span>
-<span class="sd">          Signal on which to calculate the hilbert envelope. The calculation</span>
-<span class="sd">        is done along the last axis (i.e. ``axis=-1``).</span>
-<span class="sd">    :param n_fft: The FFT size to use. If not provided, it will be determined</span>
-<span class="sd">        from the length of the signal.</span>
-<span class="sd">    :return: The Hilbert envelope of the signal.</span>
-
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">signal_length</span> <span class="o">=</span> <span class="n">signal</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">n_fft</span><span class="p">:</span>
-        <span class="n">n_fft</span> <span class="o">=</span> <span class="n">next_pow_2</span><span class="p">(</span><span class="n">signal_length</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span>
-        <span class="n">hilbert</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">n_fft</span><span class="p">)[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">signal_length</span><span class="p">]</span>
-    <span class="p">)</span>  <span class="c1"># Return signal with same shape as original</span></div>
-
+    <link href='../../../../_static/pygments.css' rel='stylesheet' type='text/css'/>
+    <link href='../../../../_static/alabaster.css' rel='stylesheet' type='text/css'/>
+    <link href='../../../../_static/graphviz.css' rel='stylesheet' type='text/css'/>
+    <script data-url_root='../../../../' id='documentation_options' src='../../../../_static/documentation_options.js'></script>
+    <script src='../../../../_static/jquery.js'></script>
+    <script src='../../../../_static/underscore.js'></script>
+    <script src='../../../../_static/_sphinx_javascript_frameworks_compat.js'></script>
+    <script src='../../../../_static/doctools.js'></script>
+    <link href='Aivclab.github.io/modulation/_modules/modulation/signal_utilities/audio_utilities/filtering.html' rel='canonical'/>
+    <link href='../../../../genindex.html' rel='index' title='Index'/>
+    <link href='../../../../search.html' rel='search' title='Search'/>
 
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-    <span class="c1"># print(mel_scale(numpy.arange(9)**2))</span>
-    <span class="c1"># print(len(fft_frequencies())) # oneside</span>
-    <span class="c1"># print(len(numpy.fft.fftfreq(512)))</span>
-    <span class="k">def</span> <span class="nf">aijsda</span><span class="p">():</span>
-        <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-        <span class="n">a</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">9</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="n">b</span> <span class="o">=</span> <span class="n">pre_emphasise</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
-        <span class="n">c</span> <span class="o">=</span> <span class="n">de_emphasise</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
-        <span class="k">assert</span> <span class="n">numpy</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">c</span><span class="p">),</span> <span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
-
-        <span class="n">aijsda</span><span class="p">()</span>
-</pre></div>
+    <link href='../../../../_static/custom.css' rel='stylesheet' type='text/css'/>
 
-          </div>
-          
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+
+            <h1>Source code for modulation.signal_utilities.audio_utilities.filtering</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 11-01-2021</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>import</span> <span class='nn'>torch</span>
+<span class='kn'>from</span> <span class='nn'>scipy.signal</span> <span class='kn'>import</span> <span class='n'>hilbert</span><span class='p'>,</span> <span class='n'>lfilter</span>
+<span class='kn'>from</span> <span class='nn'>warg</span> <span class='kn'>import</span> <span class='n'>next_pow_2</span>
+
+
+<div class='viewcode-block' id='mel_scale'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.audio_utilities.filtering.mel_scale.html#modulation.signal_utilities.audio_utilities.filtering.mel_scale'>[docs]</a><span class='k'>def</span> <span class='nf'>mel_scale</span><span class='p'>(</span><span class='n'>x</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param x:</span>
+<span class='sd'>    :type x:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>return</span> <span class='mf'>2595.0</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>log10</span><span class='p'>(</span><span class='mf'>1.0</span> <span class='o'>+</span> <span class='n'>x</span> <span class='o'>/</span> <span class='mf'>700.0</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='in_mel_scale'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.audio_utilities.filtering.in_mel_scale.html#modulation.signal_utilities.audio_utilities.filtering.in_mel_scale'>[docs]</a><span class='k'>def</span> <span class='nf'>in_mel_scale</span><span class='p'>(</span><span class='n'>x</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param x:</span>
+<span class='sd'>    :type x:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>return</span> <span class='mi'>700</span> <span class='o'>*</span> <span class='p'>(</span><span class='mi'>10</span> <span class='o'>**</span> <span class='p'>(</span><span class='n'>x</span> <span class='o'>/</span> <span class='mf'>2595.0</span><span class='p'>)</span> <span class='o'>-</span> <span class='mi'>1</span><span class='p'>)</span></div>
 
 
+<div class='viewcode-block' id='pre_emphasise'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.audio_utilities.filtering.pre_emphasise.html#modulation.signal_utilities.audio_utilities.filtering.pre_emphasise'>[docs]</a><span class='k'>def</span> <span class='nf'>pre_emphasise</span><span class='p'>(</span><span class='n'>x</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='n'>coeff</span><span class='p'>:</span> <span class='nb'>float</span> <span class='o'>=</span> <span class='mf'>0.97</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
 
+<span class='sd'>    :param x:</span>
+<span class='sd'>    :type x:</span>
+<span class='sd'>    :param coeff:</span>
+<span class='sd'>    :type coeff:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>return</span> <span class='n'>lfilter</span><span class='p'>([</span><span class='mi'>1</span><span class='p'>,</span> <span class='o'>-</span><span class='n'>coeff</span><span class='p'>],</span> <span class='p'>[</span><span class='mi'>1</span><span class='p'>],</span> <span class='n'>x</span><span class='p'>)</span></div>
 
 
+<div class='viewcode-block' id='de_emphasise'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.audio_utilities.filtering.de_emphasise.html#modulation.signal_utilities.audio_utilities.filtering.de_emphasise'>[docs]</a><span class='k'>def</span> <span class='nf'>de_emphasise</span><span class='p'>(</span><span class='n'>x</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='n'>coeff</span><span class='p'>:</span> <span class='nb'>float</span> <span class='o'>=</span> <span class='mf'>0.97</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
 
+<span class='sd'>    :param x:</span>
+<span class='sd'>    :type x:</span>
+<span class='sd'>    :param coeff:</span>
+<span class='sd'>    :type coeff:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>return</span> <span class='n'>lfilter</span><span class='p'>([</span><span class='mi'>1</span><span class='p'>],</span> <span class='p'>[</span><span class='mi'>1</span><span class='p'>,</span> <span class='o'>-</span><span class='n'>coeff</span><span class='p'>],</span> <span class='n'>x</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='pre_emphasise_torch'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.audio_utilities.filtering.pre_emphasise_torch.html#modulation.signal_utilities.audio_utilities.filtering.pre_emphasise_torch'>[docs]</a><span class='k'>def</span> <span class='nf'>pre_emphasise_torch</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='n'>coeff</span><span class='p'>:</span> <span class='nb'>float</span> <span class='o'>=</span> <span class='mf'>0.97</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    Pre-emphasis on the input signal</span>
+<span class='sd'>    :param signal: (time,)</span>
+<span class='sd'>    :param coeff:</span>
+<span class='sd'>    :return: (time,)</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>return</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>cat</span><span class='p'>((</span><span class='n'>signal</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>:</span><span class='mi'>1</span><span class='p'>],</span> <span class='n'>signal</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:]</span> <span class='o'>-</span> <span class='n'>coeff</span> <span class='o'>*</span> <span class='n'>signal</span><span class='p'>[:</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>]))</span></div>
+
+
+<div class='viewcode-block' id='fft_frequencies'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.audio_utilities.filtering.fft_frequencies.html#modulation.signal_utilities.audio_utilities.filtering.fft_frequencies'>[docs]</a><span class='k'>def</span> <span class='nf'>fft_frequencies</span><span class='p'>(</span><span class='n'>sr</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='mi'>16000</span><span class='p'>,</span> <span class='n'>n_fft</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='mi'>512</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param sr:</span>
+<span class='sd'>    :type sr:</span>
+<span class='sd'>    :param n_fft:</span>
+<span class='sd'>    :type n_fft:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>return</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>linspace</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='nb'>float</span><span class='p'>(</span><span class='n'>sr</span><span class='p'>)</span> <span class='o'>/</span> <span class='mi'>2</span><span class='p'>,</span> <span class='nb'>int</span><span class='p'>(</span><span class='mi'>1</span> <span class='o'>+</span> <span class='n'>n_fft</span> <span class='o'>//</span> <span class='mi'>2</span><span class='p'>),</span> <span class='n'>endpoint</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='hilbert_envelope'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.audio_utilities.filtering.hilbert_envelope.html#modulation.signal_utilities.audio_utilities.filtering.hilbert_envelope'>[docs]</a><span class='k'>def</span> <span class='nf'>hilbert_envelope</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='o'>*</span><span class='p'>,</span> <span class='n'>n_fft</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;Calculates the Hilbert envelope of a signal.</span>
+
+<span class='sd'>    :param signal: The signal to calculate the Hilbert envelope of.</span>
+<span class='sd'>          Signal on which to calculate the hilbert envelope. The calculation</span>
+<span class='sd'>        is done along the last axis (i.e. ``axis=-1``).</span>
+<span class='sd'>    :param n_fft: The FFT size to use. If not provided, it will be determined</span>
+<span class='sd'>        from the length of the signal.</span>
+<span class='sd'>    :return: The Hilbert envelope of the signal.</span>
+
+
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>signal_length</span> <span class='o'>=</span> <span class='n'>signal</span><span class='o'>.</span><span class='n'>shape</span><span class='p'>[</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>]</span>
+    <span class='k'>if</span> <span class='ow'>not</span> <span class='n'>n_fft</span><span class='p'>:</span>
+        <span class='n'>n_fft</span> <span class='o'>=</span> <span class='n'>next_pow_2</span><span class='p'>(</span><span class='n'>signal_length</span><span class='p'>)</span>
+    <span class='k'>return</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>abs</span><span class='p'>(</span>
+        <span class='n'>hilbert</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>,</span> <span class='n'>n_fft</span><span class='p'>)[</span><span class='o'>...</span><span class='p'>,</span> <span class='p'>:</span><span class='n'>signal_length</span><span class='p'>]</span>
+    <span class='p'>)</span>  <span class='c1'># Return signal with same shape as original</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+    <span class='c1'># print(mel_scale(numpy.arange(9)**2))</span>
+    <span class='c1'># print(len(fft_frequencies())) # oneside</span>
+    <span class='c1'># print(len(numpy.fft.fftfreq(512)))</span>
+    <span class='k'>def</span> <span class='nf'>aijsda</span><span class='p'>():</span>
+        <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+        <span class='n'>a</span> <span class='o'>=</span> <span class='nb'>list</span><span class='p'>(</span><span class='nb'>range</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>,</span> <span class='mi'>9</span> <span class='o'>+</span> <span class='mi'>1</span><span class='p'>))</span>
+        <span class='n'>b</span> <span class='o'>=</span> <span class='n'>pre_emphasise</span><span class='p'>(</span><span class='n'>a</span><span class='p'>)</span>
+        <span class='n'>c</span> <span class='o'>=</span> <span class='n'>de_emphasise</span><span class='p'>(</span><span class='n'>b</span><span class='p'>)</span>
+        <span class='k'>assert</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>allclose</span><span class='p'>(</span><span class='n'>a</span><span class='p'>,</span> <span class='n'>c</span><span class='p'>),</span> <span class='nb'>print</span><span class='p'>(</span><span class='n'>a</span><span class='p'>,</span> <span class='s2'>&quot;</span><span class='se'>\n</span><span class='s2'>&quot;</span><span class='p'>,</span> <span class='n'>c</span><span class='p'>)</span>
+
+        <span class='n'>aijsda</span><span class='p'>()</span>
+</pre>
+            </div>
+
+          </div>
 
         </div>
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+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
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+            <img alt='Logo' class='logo' src='../../../../_static/mod.svg'/>
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+          <h1 class='logo'><a href='../../../../index.html'>modulation</a></h1>
+
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+          <h3>Navigation</h3>
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../../getting_started.html'>Getting Started</a></li>
+          </ul>
+
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+            <ul>
+              <li><a href='../../../../index.html'>Documentation overview</a>
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+                  <li><a href='../../../index.html'>Module code</a>
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+                      <li><a href='../../../modulation.html'>modulation</a>
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+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
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diff --git a/docs/github/html/_modules/modulation/signal_utilities/audio_utilities/persistence.html b/docs/github/html/_modules/modulation/signal_utilities/audio_utilities/persistence.html
index 8fd2d72..b61a66d 100644
--- a/docs/github/html/_modules/modulation/signal_utilities/audio_utilities/persistence.html
+++ b/docs/github/html/_modules/modulation/signal_utilities/audio_utilities/persistence.html
@@ -1,195 +1,189 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
     <title>modulation.signal_utilities.audio_utilities.persistence &#8212; modulation 0.0.1 documentation</title>
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-            
-  <h1>Source code for modulation.signal_utilities.audio_utilities.persistence</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 17-12-2020</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">soundfile</span>
-
-
-<div class="viewcode-block" id="audio_read"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.audio_utilities.persistence.audio_read.html#modulation.signal_utilities.audio_utilities.persistence.audio_read">[docs]</a><span class="k">def</span> <span class="nf">audio_read</span><span class="p">(</span>
-    <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">norm</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">start</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">stop</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">int</span><span class="p">]:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param path:</span>
-<span class="sd">    :type path:</span>
-<span class="sd">    :param norm:</span>
-<span class="sd">    :type norm:</span>
-<span class="sd">    :param start:</span>
-<span class="sd">    :type start:</span>
-<span class="sd">    :param stop:</span>
-<span class="sd">    :type stop:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;[</span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s2">] does not exist!&quot;</span><span class="p">)</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="n">x</span><span class="p">,</span> <span class="n">sr</span> <span class="o">=</span> <span class="n">soundfile</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="n">stop</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>  <span class="c1"># mono</span>
-            <span class="k">if</span> <span class="n">norm</span><span class="p">:</span>
-                <span class="n">rms</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">**</span> <span class="mf">0.5</span>
-                <span class="n">scalar</span> <span class="o">=</span> <span class="mi">10</span> <span class="o">**</span> <span class="p">(</span><span class="o">-</span><span class="mi">25</span> <span class="o">/</span> <span class="mi">20</span><span class="p">)</span> <span class="o">/</span> <span class="n">rms</span>
-                <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="n">scalar</span>
-            <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">sr</span>
-        <span class="k">else</span><span class="p">:</span>  <span class="c1"># multi-channel</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">T</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">norm</span><span class="p">:</span>
-                <span class="n">rms</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">**</span> <span class="mf">0.5</span>
-                <span class="n">scalar</span> <span class="o">=</span> <span class="mi">10</span> <span class="o">**</span> <span class="p">(</span><span class="o">-</span><span class="mi">25</span> <span class="o">/</span> <span class="mi">20</span><span class="p">)</span> <span class="o">/</span> <span class="n">rms</span>
-                <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="n">scalar</span>
-            <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">sr</span>
-    <span class="k">except</span> <span class="ne">RuntimeError</span><span class="p">:</span>  <span class="c1"># fix for sph pcm-embedded shortened v2</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;WARNING: Audio type not supported&quot;</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="audio_write"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.audio_utilities.persistence.audio_write.html#modulation.signal_utilities.audio_utilities.persistence.audio_write">[docs]</a><span class="k">def</span> <span class="nf">audio_write</span><span class="p">(</span>
-    <span class="n">data</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">fs</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">dest_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">norm</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param data:</span>
-<span class="sd">    :type data:</span>
-<span class="sd">    :param fs:</span>
-<span class="sd">    :type fs:</span>
-<span class="sd">    :param dest_path:</span>
-<span class="sd">    :type dest_path:</span>
-<span class="sd">    :param norm:</span>
-<span class="sd">    :type norm:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">norm</span><span class="p">:</span>
-        <span class="n">rms</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">**</span> <span class="mf">0.5</span>
-        <span class="n">scalar</span> <span class="o">=</span> <span class="mi">10</span> <span class="o">**</span> <span class="p">(</span><span class="o">-</span><span class="mi">25</span> <span class="o">/</span> <span class="mi">10</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">rms</span> <span class="o">+</span> <span class="n">numpy</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">data</span> <span class="o">*</span> <span class="n">scalar</span>
-        <span class="k">if</span> <span class="nb">max</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">data</span><span class="p">))</span> <span class="o">&gt;=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span> <span class="o">/</span> <span class="nb">max</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span>
-
-    <span class="n">dest_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">dest_path</span><span class="p">)</span>
-    <span class="n">destdir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">dest_path</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">destdir</span><span class="p">):</span>
-        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">destdir</span><span class="p">)</span>
-
-    <span class="n">soundfile</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">dest_path</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">fs</span><span class="p">)</span></div>
-</pre></div>
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+    <div class='document'>
+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
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+
+          <div class='body' role='main'>
+
+            <h1>Source code for modulation.signal_utilities.audio_utilities.persistence</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 17-12-2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>import</span> <span class='nn'>os</span>
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Tuple</span>
+
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>import</span> <span class='nn'>soundfile</span>
+
+
+<div class='viewcode-block' id='audio_read'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.audio_utilities.persistence.audio_read.html#modulation.signal_utilities.audio_utilities.persistence.audio_read'>[docs]</a><span class='k'>def</span> <span class='nf'>audio_read</span><span class='p'>(</span>
+    <span class='n'>path</span><span class='p'>:</span> <span class='nb'>str</span><span class='p'>,</span> <span class='n'>norm</span><span class='p'>:</span> <span class='nb'>bool</span> <span class='o'>=</span> <span class='kc'>True</span><span class='p'>,</span> <span class='n'>start</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='mi'>0</span><span class='p'>,</span> <span class='n'>stop</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Tuple</span><span class='p'>[</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='nb'>int</span><span class='p'>]:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param path:</span>
+<span class='sd'>    :type path:</span>
+<span class='sd'>    :param norm:</span>
+<span class='sd'>    :type norm:</span>
+<span class='sd'>    :param start:</span>
+<span class='sd'>    :type start:</span>
+<span class='sd'>    :param stop:</span>
+<span class='sd'>    :type stop:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>path</span> <span class='o'>=</span> <span class='n'>os</span><span class='o'>.</span><span class='n'>path</span><span class='o'>.</span><span class='n'>abspath</span><span class='p'>(</span><span class='n'>path</span><span class='p'>)</span>
+    <span class='k'>if</span> <span class='ow'>not</span> <span class='n'>os</span><span class='o'>.</span><span class='n'>path</span><span class='o'>.</span><span class='n'>exists</span><span class='p'>(</span><span class='n'>path</span><span class='p'>):</span>
+        <span class='k'>raise</span> <span class='ne'>ValueError</span><span class='p'>(</span><span class='sa'>f</span><span class='s2'>&quot;[</span><span class='si'>{</span><span class='n'>path</span><span class='si'>}</span><span class='s2'>] does not exist!&quot;</span><span class='p'>)</span>
+    <span class='k'>try</span><span class='p'>:</span>
+        <span class='n'>x</span><span class='p'>,</span> <span class='n'>sr</span> <span class='o'>=</span> <span class='n'>soundfile</span><span class='o'>.</span><span class='n'>read</span><span class='p'>(</span><span class='n'>path</span><span class='p'>,</span> <span class='n'>start</span><span class='o'>=</span><span class='n'>start</span><span class='p'>,</span> <span class='n'>stop</span><span class='o'>=</span><span class='n'>stop</span><span class='p'>)</span>
+        <span class='k'>if</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>x</span><span class='o'>.</span><span class='n'>shape</span><span class='p'>)</span> <span class='o'>==</span> <span class='mi'>1</span><span class='p'>:</span>  <span class='c1'># mono</span>
+            <span class='k'>if</span> <span class='n'>norm</span><span class='p'>:</span>
+                <span class='n'>rms</span> <span class='o'>=</span> <span class='p'>(</span><span class='n'>x</span><span class='o'>**</span><span class='mi'>2</span><span class='p'>)</span><span class='o'>.</span><span class='n'>mean</span><span class='p'>()</span> <span class='o'>**</span> <span class='mf'>0.5</span>
+                <span class='n'>scalar</span> <span class='o'>=</span> <span class='mi'>10</span> <span class='o'>**</span> <span class='p'>(</span><span class='o'>-</span><span class='mi'>25</span> <span class='o'>/</span> <span class='mi'>20</span><span class='p'>)</span> <span class='o'>/</span> <span class='n'>rms</span>
+                <span class='n'>x</span> <span class='o'>=</span> <span class='n'>x</span> <span class='o'>*</span> <span class='n'>scalar</span>
+            <span class='k'>return</span> <span class='n'>x</span><span class='p'>,</span> <span class='n'>sr</span>
+        <span class='k'>else</span><span class='p'>:</span>  <span class='c1'># multi-channel</span>
+            <span class='n'>x</span> <span class='o'>=</span> <span class='n'>x</span><span class='o'>.</span><span class='n'>T</span>
+            <span class='n'>x</span> <span class='o'>=</span> <span class='n'>x</span><span class='o'>.</span><span class='n'>sum</span><span class='p'>(</span><span class='n'>axis</span><span class='o'>=</span><span class='mi'>0</span><span class='p'>)</span> <span class='o'>/</span> <span class='n'>x</span><span class='o'>.</span><span class='n'>shape</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span>
+            <span class='k'>if</span> <span class='n'>norm</span><span class='p'>:</span>
+                <span class='n'>rms</span> <span class='o'>=</span> <span class='p'>(</span><span class='n'>x</span><span class='o'>**</span><span class='mi'>2</span><span class='p'>)</span><span class='o'>.</span><span class='n'>mean</span><span class='p'>()</span> <span class='o'>**</span> <span class='mf'>0.5</span>
+                <span class='n'>scalar</span> <span class='o'>=</span> <span class='mi'>10</span> <span class='o'>**</span> <span class='p'>(</span><span class='o'>-</span><span class='mi'>25</span> <span class='o'>/</span> <span class='mi'>20</span><span class='p'>)</span> <span class='o'>/</span> <span class='n'>rms</span>
+                <span class='n'>x</span> <span class='o'>=</span> <span class='n'>x</span> <span class='o'>*</span> <span class='n'>scalar</span>
+            <span class='k'>return</span> <span class='n'>x</span><span class='p'>,</span> <span class='n'>sr</span>
+    <span class='k'>except</span> <span class='ne'>RuntimeError</span><span class='p'>:</span>  <span class='c1'># fix for sph pcm-embedded shortened v2</span>
+        <span class='nb'>print</span><span class='p'>(</span><span class='s2'>&quot;WARNING: Audio type not supported&quot;</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='audio_write'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.audio_utilities.persistence.audio_write.html#modulation.signal_utilities.audio_utilities.persistence.audio_write'>[docs]</a><span class='k'>def</span> <span class='nf'>audio_write</span><span class='p'>(</span>
+    <span class='n'>data</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='n'>fs</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span> <span class='n'>dest_path</span><span class='p'>:</span> <span class='nb'>str</span><span class='p'>,</span> <span class='n'>norm</span><span class='p'>:</span> <span class='nb'>bool</span> <span class='o'>=</span> <span class='kc'>False</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='kc'>None</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param data:</span>
+<span class='sd'>    :type data:</span>
+<span class='sd'>    :param fs:</span>
+<span class='sd'>    :type fs:</span>
+<span class='sd'>    :param dest_path:</span>
+<span class='sd'>    :type dest_path:</span>
+<span class='sd'>    :param norm:</span>
+<span class='sd'>    :type norm:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>if</span> <span class='n'>norm</span><span class='p'>:</span>
+        <span class='n'>rms</span> <span class='o'>=</span> <span class='p'>(</span><span class='n'>data</span><span class='o'>**</span><span class='mi'>2</span><span class='p'>)</span><span class='o'>.</span><span class='n'>mean</span><span class='p'>()</span> <span class='o'>**</span> <span class='mf'>0.5</span>
+        <span class='n'>scalar</span> <span class='o'>=</span> <span class='mi'>10</span> <span class='o'>**</span> <span class='p'>(</span><span class='o'>-</span><span class='mi'>25</span> <span class='o'>/</span> <span class='mi'>10</span><span class='p'>)</span> <span class='o'>/</span> <span class='p'>(</span><span class='n'>rms</span> <span class='o'>+</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>eps</span><span class='p'>)</span>
+        <span class='n'>data</span> <span class='o'>=</span> <span class='n'>data</span> <span class='o'>*</span> <span class='n'>scalar</span>
+        <span class='k'>if</span> <span class='nb'>max</span><span class='p'>(</span><span class='nb'>abs</span><span class='p'>(</span><span class='n'>data</span><span class='p'>))</span> <span class='o'>&gt;=</span> <span class='mi'>1</span><span class='p'>:</span>
+            <span class='n'>data</span> <span class='o'>=</span> <span class='n'>data</span> <span class='o'>/</span> <span class='nb'>max</span><span class='p'>(</span><span class='nb'>abs</span><span class='p'>(</span><span class='n'>data</span><span class='p'>),</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>eps</span><span class='p'>)</span>
+
+    <span class='n'>dest_path</span> <span class='o'>=</span> <span class='n'>os</span><span class='o'>.</span><span class='n'>path</span><span class='o'>.</span><span class='n'>abspath</span><span class='p'>(</span><span class='n'>dest_path</span><span class='p'>)</span>
+    <span class='n'>destdir</span> <span class='o'>=</span> <span class='n'>os</span><span class='o'>.</span><span class='n'>path</span><span class='o'>.</span><span class='n'>dirname</span><span class='p'>(</span><span class='n'>dest_path</span><span class='p'>)</span>
+
+    <span class='k'>if</span> <span class='ow'>not</span> <span class='n'>os</span><span class='o'>.</span><span class='n'>path</span><span class='o'>.</span><span class='n'>exists</span><span class='p'>(</span><span class='n'>destdir</span><span class='p'>):</span>
+        <span class='n'>os</span><span class='o'>.</span><span class='n'>makedirs</span><span class='p'>(</span><span class='n'>destdir</span><span class='p'>)</span>
+
+    <span class='n'>soundfile</span><span class='o'>.</span><span class='n'>write</span><span class='p'>(</span><span class='n'>dest_path</span><span class='p'>,</span> <span class='n'>data</span><span class='p'>,</span> <span class='n'>fs</span><span class='p'>)</span></div>
+</pre>
+            </div>
 
           </div>
-          
+
         </div>
       </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
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-              <img class="logo" src="../../../../_static/mod.svg" alt="Logo"/>
-            </a></p>
-<h1 class="logo"><a href="../../../../index.html">modulation</a></h1>
-
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-
-
-<h3>Navigation</h3>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../../generated/modulation.html">modulation</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notes</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../../getting_started.html">Getting Started</a></li>
-</ul>
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-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../../../../index.html">Documentation overview</a><ul>
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-  <li><a href="../../../modulation.html">modulation</a><ul>
-  </ul></li>
-  </ul></li>
-  </ul></li>
-</ul>
-</div>
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-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../../../../search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
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-    </form>
-    </div>
-</div>
-<script>document.getElementById('searchbox').style.display = "block"</script>
-
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+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <p class='logo'><a href='../../../../index.html'>
+            <img alt='Logo' class='logo' src='../../../../_static/mod.svg'/>
+          </a></p>
+          <h1 class='logo'><a href='../../../../index.html'>modulation</a></h1>
+
+
+          <h3>Navigation</h3>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../../generated/modulation.html'>modulation</a></li>
+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../../../../index.html'>Documentation overview</a>
+                <ul>
+                  <li><a href='../../../index.html'>Module code</a>
+                    <ul>
+                      <li><a href='../../../modulation.html'>modulation</a>
+                        <ul>
+                        </ul>
+                      </li>
+                    </ul>
+                  </li>
+                </ul>
+              </li>
+            </ul>
+          </div>
+          <div id='searchbox' role='search' style='display: none'>
+            <h3 id='searchlabel'>Quick search</h3>
+            <div class='searchformwrapper'>
+              <form action='../../../../search.html' class='search' method='get'>
+                <input aria-labelledby='searchlabel' autocapitalize='off' autocomplete='off' autocorrect='off' name='q' spellcheck='false' type='text'/>
+                <input type='submit' value='Go'/>
+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
 
 
         </div>
       </div>
-      <div class="clearer"></div>
+      <div class='clearer'></div>
     </div>
-    <div class="footer">
+    <div class='footer'>
       &copy;.
-      
+
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
-      
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
+
     </div>
 
-    
 
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/docs/github/html/_modules/modulation/signal_utilities/chopping.html b/docs/github/html/_modules/modulation/signal_utilities/chopping.html
index bf76df3..629007d 100644
--- a/docs/github/html/_modules/modulation/signal_utilities/chopping.html
+++ b/docs/github/html/_modules/modulation/signal_utilities/chopping.html
@@ -1,269 +1,263 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
     <title>modulation.signal_utilities.chopping &#8212; modulation 0.0.1 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/graphviz.css" />
-    <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
-    <script src="../../../_static/jquery.js"></script>
-    <script src="../../../_static/underscore.js"></script>
-    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
-    <script src="../../../_static/doctools.js"></script>
-    <link rel="canonical" href="Aivclab.github.io/modulation/_modules/modulation/signal_utilities/chopping.html" />
-    <link rel="index" title="Index" href="../../../genindex.html" />
-    <link rel="search" title="Search" href="../../../search.html" />
-   
-  <link rel="stylesheet" href="../../../_static/custom.css" type="text/css" />
-  
-  
-  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
-
-  </head><body>
-  
-
-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          
-
-          <div class="body" role="main">
-            
-  <h1>Source code for modulation.signal_utilities.chopping</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;heider&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 7/8/22</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">&quot;chop_n&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;chop_n_end&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;chop_n_middle&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;chop_n_middle_end&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;chop_n_middle_start&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;chop_n_start&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;mask_chop_segments&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;separate_mask_regions&quot;</span><span class="p">,</span>
-<span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">Tuple</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-
-<span class="kn">from</span> <span class="nn">modulation.signal_utilities.segmentation</span> <span class="kn">import</span> <span class="n">runs_of_non_zero</span>
-
-
-<div class="viewcode-block" id="chop_n"><a class="viewcode-back" href="../../../generated/modulation.signal_utilities.chopping.chop_n.html#modulation.signal_utilities.chopping.chop_n">[docs]</a><span class="k">def</span> <span class="nf">chop_n</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Sequence</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param x:</span>
-<span class="sd">    :type x:</span>
-<span class="sd">    :param n:</span>
-<span class="sd">    :type n:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">x</span><span class="p">[:</span><span class="n">n</span><span class="p">]</span></div>
-
-
-<div class="viewcode-block" id="chop_n_end"><a class="viewcode-back" href="../../../generated/modulation.signal_utilities.chopping.chop_n_end.html#modulation.signal_utilities.chopping.chop_n_end">[docs]</a><span class="k">def</span> <span class="nf">chop_n_end</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Sequence</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param x:</span>
-<span class="sd">    :type x:</span>
-<span class="sd">    :param n:</span>
-<span class="sd">    :type n:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">x</span><span class="p">[</span><span class="o">-</span><span class="n">n</span><span class="p">:]</span></div>
-
-
-<div class="viewcode-block" id="chop_n_middle"><a class="viewcode-back" href="../../../generated/modulation.signal_utilities.chopping.chop_n_middle.html#modulation.signal_utilities.chopping.chop_n_middle">[docs]</a><span class="k">def</span> <span class="nf">chop_n_middle</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Sequence</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param x:</span>
-<span class="sd">    :type x:</span>
-<span class="sd">    :param n:</span>
-<span class="sd">    :type n:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">x</span><span class="p">[</span><span class="n">n</span><span class="p">:</span><span class="o">-</span><span class="n">n</span><span class="p">]</span></div>
-
-
-<div class="viewcode-block" id="chop_n_middle_end"><a class="viewcode-back" href="../../../generated/modulation.signal_utilities.chopping.chop_n_middle_end.html#modulation.signal_utilities.chopping.chop_n_middle_end">[docs]</a><span class="k">def</span> <span class="nf">chop_n_middle_end</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Sequence</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param x:</span>
-<span class="sd">    :type x:</span>
-<span class="sd">    :param n:</span>
-<span class="sd">    :type n:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">x</span><span class="p">[</span><span class="n">n</span><span class="p">:</span><span class="o">-</span><span class="n">n</span><span class="p">][::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span></div>
-
-
-<div class="viewcode-block" id="chop_n_middle_start"><a class="viewcode-back" href="../../../generated/modulation.signal_utilities.chopping.chop_n_middle_start.html#modulation.signal_utilities.chopping.chop_n_middle_start">[docs]</a><span class="k">def</span> <span class="nf">chop_n_middle_start</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Sequence</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
+    <link href='../../../_static/pygments.css' rel='stylesheet' type='text/css'/>
+    <link href='../../../_static/alabaster.css' rel='stylesheet' type='text/css'/>
+    <link href='../../../_static/graphviz.css' rel='stylesheet' type='text/css'/>
+    <script data-url_root='../../../' id='documentation_options' src='../../../_static/documentation_options.js'></script>
+    <script src='../../../_static/jquery.js'></script>
+    <script src='../../../_static/underscore.js'></script>
+    <script src='../../../_static/_sphinx_javascript_frameworks_compat.js'></script>
+    <script src='../../../_static/doctools.js'></script>
+    <link href='Aivclab.github.io/modulation/_modules/modulation/signal_utilities/chopping.html' rel='canonical'/>
+    <link href='../../../genindex.html' rel='index' title='Index'/>
+    <link href='../../../search.html' rel='search' title='Search'/>
 
-<span class="sd">    :param x:</span>
-<span class="sd">    :type x:</span>
-<span class="sd">    :param n:</span>
-<span class="sd">    :type n:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">x</span><span class="p">[</span><span class="n">n</span><span class="p">:</span><span class="o">-</span><span class="n">n</span><span class="p">][::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span></div>
+    <link href='../../../_static/custom.css' rel='stylesheet' type='text/css'/>
 
 
-<div class="viewcode-block" id="chop_n_start"><a class="viewcode-back" href="../../../generated/modulation.signal_utilities.chopping.chop_n_start.html#modulation.signal_utilities.chopping.chop_n_start">[docs]</a><span class="k">def</span> <span class="nf">chop_n_start</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Sequence</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
+    <meta content='width=device-width, initial-scale=0.9, maximum-scale=0.9' name='viewport'/>
 
-<span class="sd">    :param x:</span>
-<span class="sd">    :type x:</span>
-<span class="sd">    :param n:</span>
-<span class="sd">    :type n:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">x</span><span class="p">[</span><span class="n">n</span><span class="p">:]</span></div>
+  </head>
+  <body>
 
 
-<div class="viewcode-block" id="mask_chop_segments"><a class="viewcode-back" href="../../../generated/modulation.signal_utilities.chopping.mask_chop_segments.html#modulation.signal_utilities.chopping.mask_chop_segments">[docs]</a><span class="k">def</span> <span class="nf">mask_chop_segments</span><span class="p">(</span><span class="n">mask</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Sequence</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    discards rest of the data if vad is shorter than data</span>
+    <div class='document'>
+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
 
-<span class="sd">    returns consecutive segments is where the mask is 1</span>
 
-<span class="sd">    :param mask:</span>
-<span class="sd">    :type mask:</span>
-<span class="sd">    :param data:</span>
-<span class="sd">    :type data:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">data_masked</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+          <div class='body' role='main'>
 
-    <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">mask</span><span class="p">))):</span>
-        <span class="k">if</span> <span class="n">mask</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">data_masked</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">data_masked</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+            <h1>Source code for modulation.signal_utilities.chopping</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
 
-    <span class="k">return</span> <span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">r</span><span class="p">)</span> <span class="k">for</span> <span class="n">b</span><span class="p">,</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">runs_of_non_zero</span><span class="p">(</span><span class="n">data_masked</span><span class="p">)</span> <span class="k">if</span> <span class="n">b</span><span class="p">]</span></div>
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;heider&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
 
+<span class='s2'>           Created on 7/8/22</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
 
-<div class="viewcode-block" id="separate_mask_regions"><a class="viewcode-back" href="../../../generated/modulation.signal_utilities.chopping.separate_mask_regions.html#modulation.signal_utilities.chopping.separate_mask_regions">[docs]</a><span class="k">def</span> <span class="nf">separate_mask_regions</span><span class="p">(</span><span class="n">data</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">mask</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Sequence</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">]:</span>
-    <span class="n">m0</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(([</span><span class="kc">False</span><span class="p">],</span> <span class="n">mask</span><span class="p">,</span> <span class="p">[</span><span class="kc">False</span><span class="p">]))</span>
-    <span class="n">idx</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">flatnonzero</span><span class="p">(</span><span class="n">m0</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">!=</span> <span class="n">m0</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-    <span class="k">return</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">idx</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="p">:</span> <span class="n">idx</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">idx</span><span class="p">),</span> <span class="mi">2</span><span class="p">)],</span> <span class="p">[</span>
-        <span class="p">(</span><span class="n">idx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">idx</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">idx</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span>
-    <span class="p">]</span></div>
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[</span>
+    <span class='s2'>&quot;chop_n&quot;</span><span class='p'>,</span>
+    <span class='s2'>&quot;chop_n_end&quot;</span><span class='p'>,</span>
+    <span class='s2'>&quot;chop_n_middle&quot;</span><span class='p'>,</span>
+    <span class='s2'>&quot;chop_n_middle_end&quot;</span><span class='p'>,</span>
+    <span class='s2'>&quot;chop_n_middle_start&quot;</span><span class='p'>,</span>
+    <span class='s2'>&quot;chop_n_start&quot;</span><span class='p'>,</span>
+    <span class='s2'>&quot;mask_chop_segments&quot;</span><span class='p'>,</span>
+    <span class='s2'>&quot;separate_mask_regions&quot;</span><span class='p'>,</span>
+<span class='p'>]</span>
 
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>Iterable</span><span class='p'>,</span> <span class='n'>Tuple</span>
 
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+<span class='kn'>import</span> <span class='nn'>numpy</span>
 
-    <span class="k">def</span> <span class="nf">asudh</span><span class="p">():</span>
-        <span class="n">c</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-        <span class="n">c</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="n">c</span><span class="p">[</span><span class="mi">8</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+<span class='kn'>from</span> <span class='nn'>modulation.signal_utilities.segmentation</span> <span class='kn'>import</span> <span class='n'>runs_of_non_zero</span>
 
-        <span class="n">s</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
 
-        <span class="nb">print</span><span class="p">(</span><span class="n">mask_chop_segments</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">s</span><span class="p">))</span>
+<div class='viewcode-block' id='chop_n'><a class='viewcode-back' href='../../../generated/modulation.signal_utilities.chopping.chop_n.html#modulation.signal_utilities.chopping.chop_n'>[docs]</a><span class='k'>def</span> <span class='nf'>chop_n</span><span class='p'>(</span><span class='n'>x</span><span class='p'>:</span> <span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>n</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Sequence</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="nf">uahsd</span><span class="p">():</span>
-        <span class="n">a</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-        <span class="n">m</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">])</span>
+<span class='sd'>    :param x:</span>
+<span class='sd'>    :type x:</span>
+<span class='sd'>    :param n:</span>
+<span class='sd'>    :type n:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>return</span> <span class='n'>x</span><span class='p'>[:</span><span class='n'>n</span><span class='p'>]</span></div>
 
-        <span class="nb">print</span><span class="p">(</span><span class="n">separate_mask_regions</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">m</span><span class="p">))</span>
 
-    <span class="c1"># asudh()</span>
-    <span class="n">uahsd</span><span class="p">()</span>
-</pre></div>
+<div class='viewcode-block' id='chop_n_end'><a class='viewcode-back' href='../../../generated/modulation.signal_utilities.chopping.chop_n_end.html#modulation.signal_utilities.chopping.chop_n_end'>[docs]</a><span class='k'>def</span> <span class='nf'>chop_n_end</span><span class='p'>(</span><span class='n'>x</span><span class='p'>:</span> <span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>n</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Sequence</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param x:</span>
+<span class='sd'>    :type x:</span>
+<span class='sd'>    :param n:</span>
+<span class='sd'>    :type n:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>return</span> <span class='n'>x</span><span class='p'>[</span><span class='o'>-</span><span class='n'>n</span><span class='p'>:]</span></div>
+
+
+<div class='viewcode-block' id='chop_n_middle'><a class='viewcode-back' href='../../../generated/modulation.signal_utilities.chopping.chop_n_middle.html#modulation.signal_utilities.chopping.chop_n_middle'>[docs]</a><span class='k'>def</span> <span class='nf'>chop_n_middle</span><span class='p'>(</span><span class='n'>x</span><span class='p'>:</span> <span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>n</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Sequence</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param x:</span>
+<span class='sd'>    :type x:</span>
+<span class='sd'>    :param n:</span>
+<span class='sd'>    :type n:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>return</span> <span class='n'>x</span><span class='p'>[</span><span class='n'>n</span><span class='p'>:</span><span class='o'>-</span><span class='n'>n</span><span class='p'>]</span></div>
+
+
+<div class='viewcode-block' id='chop_n_middle_end'><a class='viewcode-back' href='../../../generated/modulation.signal_utilities.chopping.chop_n_middle_end.html#modulation.signal_utilities.chopping.chop_n_middle_end'>[docs]</a><span class='k'>def</span> <span class='nf'>chop_n_middle_end</span><span class='p'>(</span><span class='n'>x</span><span class='p'>:</span> <span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>n</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Sequence</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param x:</span>
+<span class='sd'>    :type x:</span>
+<span class='sd'>    :param n:</span>
+<span class='sd'>    :type n:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>return</span> <span class='n'>x</span><span class='p'>[</span><span class='n'>n</span><span class='p'>:</span><span class='o'>-</span><span class='n'>n</span><span class='p'>][::</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>]</span></div>
+
+
+<div class='viewcode-block' id='chop_n_middle_start'><a class='viewcode-back' href='../../../generated/modulation.signal_utilities.chopping.chop_n_middle_start.html#modulation.signal_utilities.chopping.chop_n_middle_start'>[docs]</a><span class='k'>def</span> <span class='nf'>chop_n_middle_start</span><span class='p'>(</span><span class='n'>x</span><span class='p'>:</span> <span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>n</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Sequence</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param x:</span>
+<span class='sd'>    :type x:</span>
+<span class='sd'>    :param n:</span>
+<span class='sd'>    :type n:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>return</span> <span class='n'>x</span><span class='p'>[</span><span class='n'>n</span><span class='p'>:</span><span class='o'>-</span><span class='n'>n</span><span class='p'>][::</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>]</span></div>
+
+
+<div class='viewcode-block' id='chop_n_start'><a class='viewcode-back' href='../../../generated/modulation.signal_utilities.chopping.chop_n_start.html#modulation.signal_utilities.chopping.chop_n_start'>[docs]</a><span class='k'>def</span> <span class='nf'>chop_n_start</span><span class='p'>(</span><span class='n'>x</span><span class='p'>:</span> <span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>n</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Sequence</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param x:</span>
+<span class='sd'>    :type x:</span>
+<span class='sd'>    :param n:</span>
+<span class='sd'>    :type n:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>return</span> <span class='n'>x</span><span class='p'>[</span><span class='n'>n</span><span class='p'>:]</span></div>
 
-          </div>
-          
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-            <p class="logo"><a href="../../../index.html">
-              <img class="logo" src="../../../_static/mod.svg" alt="Logo"/>
-            </a></p>
-<h1 class="logo"><a href="../../../index.html">modulation</a></h1>
-
-
-
-
-
-
-
-
-<h3>Navigation</h3>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../generated/modulation.html">modulation</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notes</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../getting_started.html">Getting Started</a></li>
-</ul>
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-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../../../index.html">Documentation overview</a><ul>
-  <li><a href="../../index.html">Module code</a><ul>
-  <li><a href="../../modulation.html">modulation</a><ul>
-  </ul></li>
-  </ul></li>
-  </ul></li>
-</ul>
-</div>
-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../../../search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
-      <input type="submit" value="Go" />
-    </form>
-    </div>
-</div>
-<script>document.getElementById('searchbox').style.display = "block"</script>
 
+<div class='viewcode-block' id='mask_chop_segments'><a class='viewcode-back' href='../../../generated/modulation.signal_utilities.chopping.mask_chop_segments.html#modulation.signal_utilities.chopping.mask_chop_segments'>[docs]</a><span class='k'>def</span> <span class='nf'>mask_chop_segments</span><span class='p'>(</span><span class='n'>mask</span><span class='p'>:</span> <span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>data</span><span class='p'>:</span> <span class='n'>Sequence</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Sequence</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    discards rest of the data if vad is shorter than data</span>
 
+<span class='sd'>    returns consecutive segments is where the mask is 1</span>
 
+<span class='sd'>    :param mask:</span>
+<span class='sd'>    :type mask:</span>
+<span class='sd'>    :param data:</span>
+<span class='sd'>    :type data:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>data_masked</span> <span class='o'>=</span> <span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span> <span class='o'>*</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>data</span><span class='p'>)</span>
 
+    <span class='k'>for</span> <span class='n'>n</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='nb'>min</span><span class='p'>(</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>data</span><span class='p'>),</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>mask</span><span class='p'>))):</span>
+        <span class='k'>if</span> <span class='n'>mask</span><span class='p'>[</span><span class='n'>n</span><span class='p'>]</span> <span class='o'>!=</span> <span class='mi'>0</span><span class='p'>:</span>
+            <span class='n'>data_masked</span><span class='p'>[</span><span class='n'>n</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>data</span><span class='p'>[</span><span class='n'>n</span><span class='p'>]</span>
+        <span class='k'>else</span><span class='p'>:</span>
+            <span class='n'>data_masked</span><span class='p'>[</span><span class='n'>n</span><span class='p'>]</span> <span class='o'>=</span> <span class='mi'>0</span>
 
+    <span class='k'>return</span> <span class='p'>[</span><span class='nb'>list</span><span class='p'>(</span><span class='n'>r</span><span class='p'>)</span> <span class='k'>for</span> <span class='n'>b</span><span class='p'>,</span> <span class='n'>r</span> <span class='ow'>in</span> <span class='n'>runs_of_non_zero</span><span class='p'>(</span><span class='n'>data_masked</span><span class='p'>)</span> <span class='k'>if</span> <span class='n'>b</span><span class='p'>]</span></div>
 
 
+<div class='viewcode-block' id='separate_mask_regions'><a class='viewcode-back' href='../../../generated/modulation.signal_utilities.chopping.separate_mask_regions.html#modulation.signal_utilities.chopping.separate_mask_regions'>[docs]</a><span class='k'>def</span> <span class='nf'>separate_mask_regions</span><span class='p'>(</span><span class='n'>data</span><span class='p'>:</span> <span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>mask</span><span class='p'>:</span> <span class='n'>Sequence</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Tuple</span><span class='p'>[</span><span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>Sequence</span><span class='p'>]:</span>
+    <span class='n'>m0</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>concatenate</span><span class='p'>(([</span><span class='kc'>False</span><span class='p'>],</span> <span class='n'>mask</span><span class='p'>,</span> <span class='p'>[</span><span class='kc'>False</span><span class='p'>]))</span>
+    <span class='n'>idx</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>flatnonzero</span><span class='p'>(</span><span class='n'>m0</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:]</span> <span class='o'>!=</span> <span class='n'>m0</span><span class='p'>[:</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>])</span>
+    <span class='k'>return</span> <span class='p'>[</span><span class='n'>data</span><span class='p'>[</span><span class='n'>idx</span><span class='p'>[</span><span class='n'>i</span><span class='p'>]</span> <span class='p'>:</span> <span class='n'>idx</span><span class='p'>[</span><span class='n'>i</span> <span class='o'>+</span> <span class='mi'>1</span><span class='p'>]]</span> <span class='k'>for</span> <span class='n'>i</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>idx</span><span class='p'>),</span> <span class='mi'>2</span><span class='p'>)],</span> <span class='p'>[</span>
+        <span class='p'>(</span><span class='n'>idx</span><span class='p'>[</span><span class='n'>i</span><span class='p'>],</span> <span class='n'>idx</span><span class='p'>[</span><span class='n'>i</span> <span class='o'>+</span> <span class='mi'>1</span><span class='p'>])</span> <span class='k'>for</span> <span class='n'>i</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>idx</span><span class='p'>),</span> <span class='mi'>2</span><span class='p'>)</span>
+    <span class='p'>]</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+
+    <span class='k'>def</span> <span class='nf'>asudh</span><span class='p'>():</span>
+        <span class='n'>c</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>zeros</span><span class='p'>(</span><span class='mi'>10</span><span class='p'>)</span>
+        <span class='n'>c</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:</span><span class='mi'>5</span><span class='p'>]</span> <span class='o'>=</span> <span class='mi'>1</span>
+        <span class='n'>c</span><span class='p'>[</span><span class='mi'>8</span><span class='p'>:</span><span class='mi'>10</span><span class='p'>]</span> <span class='o'>=</span> <span class='mi'>1</span>
+
+        <span class='n'>s</span> <span class='o'>=</span> <span class='nb'>list</span><span class='p'>(</span><span class='nb'>range</span><span class='p'>(</span><span class='mi'>10</span><span class='p'>))</span>
+        <span class='nb'>print</span><span class='p'>(</span><span class='n'>c</span><span class='p'>,</span> <span class='n'>s</span><span class='p'>)</span>
+
+        <span class='nb'>print</span><span class='p'>(</span><span class='n'>mask_chop_segments</span><span class='p'>(</span><span class='n'>c</span><span class='p'>,</span> <span class='n'>s</span><span class='p'>))</span>
+
+    <span class='k'>def</span> <span class='nf'>uahsd</span><span class='p'>():</span>
+        <span class='n'>a</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>random</span><span class='o'>.</span><span class='n'>random</span><span class='p'>((</span><span class='mi'>5</span><span class='p'>,</span> <span class='mi'>2</span><span class='p'>))</span>
+        <span class='n'>m</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>([</span><span class='kc'>True</span><span class='p'>,</span> <span class='kc'>True</span><span class='p'>,</span> <span class='kc'>False</span><span class='p'>,</span> <span class='kc'>True</span><span class='p'>,</span> <span class='kc'>True</span><span class='p'>])</span>
+
+        <span class='nb'>print</span><span class='p'>(</span><span class='n'>separate_mask_regions</span><span class='p'>(</span><span class='n'>a</span><span class='p'>,</span> <span class='n'>m</span><span class='p'>))</span>
+
+    <span class='c1'># asudh()</span>
+    <span class='n'>uahsd</span><span class='p'>()</span>
+</pre>
+            </div>
+
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+++ b/docs/github/html/_modules/modulation/signal_utilities/noise_generation/additive_noise.html
@@ -1,236 +1,230 @@
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-  <h1>Source code for modulation.signal_utilities.noise_generation.additive_noise</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 10-12-2020</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">from</span> <span class="nn">draugr.visualisation</span> <span class="kn">import</span> <span class="n">progress_bar</span>
-<span class="kn">from</span> <span class="nn">warg</span> <span class="kn">import</span> <span class="n">ensure_existence</span>
-<span class="kn">from</span> <span class="nn">draugr.numpy_utilities</span> <span class="kn">import</span> <span class="n">root_mean_square</span>
-
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">signal</span>
-<span class="kn">from</span> <span class="nn">scipy.io</span> <span class="kn">import</span> <span class="n">wavfile</span>
-
-<span class="kn">from</span> <span class="nn">modulation.signal_utilities</span> <span class="kn">import</span> <span class="n">mask_split_non_zero_concat</span>
-
-
-<div class="viewcode-block" id="sample_noise"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.noise_generation.additive_noise.sample_noise.html#modulation.signal_utilities.noise_generation.additive_noise.sample_noise">[docs]</a><span class="k">def</span> <span class="nf">sample_noise</span><span class="p">(</span>
-    <span class="n">noise</span><span class="p">,</span>
-    <span class="n">noise_rate</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-    <span class="n">signal_len</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-    <span class="n">signal_rate</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">resample_noise</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param noise:</span>
-<span class="sd">    :type noise:</span>
-<span class="sd">    :param noise_rate:</span>
-<span class="sd">    :type noise_rate:</span>
-<span class="sd">    :param signal_len:</span>
-<span class="sd">    :type signal_len:</span>
-<span class="sd">    :param signal_rate:</span>
-<span class="sd">    :type signal_rate:</span>
-<span class="sd">    :param resample_noise:</span>
-<span class="sd">    :type resample_noise:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">noise_len</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">noise</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">resample_noise</span><span class="p">:</span>
-        <span class="n">noise</span> <span class="o">=</span> <span class="n">signal</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span>
-            <span class="n">noise</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">noise_len</span> <span class="o">*</span> <span class="nb">float</span><span class="p">(</span><span class="n">signal_rate</span><span class="p">)</span> <span class="o">/</span> <span class="n">noise_rate</span><span class="p">)</span>
-        <span class="p">)</span>
-
-    <span class="n">start_index</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">noise_len</span><span class="p">)</span>
-    <span class="n">noise</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">noise</span><span class="p">,</span> <span class="p">(</span><span class="n">signal_len</span> <span class="o">//</span> <span class="n">noise_len</span><span class="p">)</span> <span class="o">+</span> <span class="mi">2</span><span class="p">)</span>  <span class="c1"># at least tile once (=2)</span>
-    <span class="k">return</span> <span class="n">noise</span><span class="p">[</span><span class="n">start_index</span> <span class="p">:</span> <span class="n">signal_len</span> <span class="o">+</span> <span class="n">start_index</span><span class="p">]</span></div>
-
-
-<div class="viewcode-block" id="compute_additive_noise_samples"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.noise_generation.additive_noise.compute_additive_noise_samples.html#modulation.signal_utilities.noise_generation.additive_noise.compute_additive_noise_samples">[docs]</a><span class="k">def</span> <span class="nf">compute_additive_noise_samples</span><span class="p">(</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">voice_activity_mask</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
-    <span class="n">signal_file</span><span class="p">:</span> <span class="n">Path</span><span class="p">,</span>
-    <span class="n">category</span><span class="p">,</span>
-    <span class="n">out_dir</span><span class="p">,</span>
-    <span class="n">noise_file</span><span class="p">,</span>
-    <span class="n">snrs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param voice_activity_mask:</span>
-<span class="sd">    :type voice_activity_mask:</span>
-<span class="sd">    :param signal_file:</span>
-<span class="sd">    :type signal_file:</span>
-<span class="sd">    :param category:</span>
-<span class="sd">    :type category:</span>
-<span class="sd">    :param out_dir:</span>
-<span class="sd">    :type out_dir:</span>
-<span class="sd">    :param noise_file:</span>
-<span class="sd">    :type noise_file:</span>
-<span class="sd">    :param snrs:</span>
-<span class="sd">    :type snrs:</span>
-<span class="sd">    :param verbose:</span>
-<span class="sd">    :type verbose:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">snrs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">snrs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">((</span><span class="n">i</span> <span class="o">*</span> <span class="mi">5</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)))</span>
-
-    <span class="n">sr_noise</span><span class="p">,</span> <span class="n">noise</span> <span class="o">=</span> <span class="n">wavfile</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">noise_file</span><span class="p">))</span>
-    <span class="n">sr_signal</span><span class="p">,</span> <span class="n">signal</span> <span class="o">=</span> <span class="n">wavfile</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">signal_file</span><span class="p">))</span>
-
-    <span class="n">max_sample</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">signal</span><span class="p">)</span>
-    <span class="n">signal</span> <span class="o">=</span> <span class="n">signal</span> <span class="o">/</span> <span class="n">max_sample</span>
-
-    <span class="n">noise_part</span> <span class="o">=</span> <span class="n">sample_noise</span><span class="p">(</span>
-        <span class="n">noise</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">noise</span><span class="p">),</span>
-        <span class="n">noise_rate</span><span class="o">=</span><span class="n">sr_noise</span><span class="p">,</span>
-        <span class="n">signal_len</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">signal</span><span class="p">),</span>
-        <span class="n">signal_rate</span><span class="o">=</span><span class="n">sr_signal</span><span class="p">,</span>
-    <span class="p">)</span>
-
-    <span class="n">noise_scaled</span> <span class="o">=</span> <span class="n">noise_part</span> <span class="o">*</span> <span class="p">(</span>
-        <span class="n">root_mean_square</span><span class="p">(</span><span class="n">mask_split_non_zero_concat</span><span class="p">(</span><span class="n">voice_activity_mask</span><span class="p">,</span> <span class="n">signal</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
-        <span class="o">/</span> <span class="n">root_mean_square</span><span class="p">(</span><span class="n">noise_part</span><span class="p">)</span>
-    <span class="p">)</span>  <span class="c1"># scaled by ratio of speech to noise level</span>
-
-    <span class="k">for</span> <span class="n">snr</span> <span class="ow">in</span> <span class="n">progress_bar</span><span class="p">(</span><span class="n">snrs</span><span class="p">,</span> <span class="n">disable</span><span class="o">=</span><span class="ow">not</span> <span class="n">verbose</span><span class="p">):</span>
-        <span class="n">noised</span> <span class="o">=</span> <span class="n">signal</span> <span class="o">+</span> <span class="n">noise_scaled</span> <span class="o">/</span> <span class="p">(</span><span class="mi">10</span> <span class="o">**</span> <span class="p">(</span><span class="n">snr</span> <span class="o">/</span> <span class="mi">20</span><span class="p">))</span>
-        <span class="n">dest</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span>
-            <span class="n">ensure_existence</span><span class="p">(</span>
-                <span class="n">out_dir</span> <span class="o">/</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">noise_file</span><span class="o">.</span><span class="n">with_suffix</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s1">_SNR_</span><span class="si">{</span><span class="n">snr</span><span class="si">}</span><span class="s1">dB&#39;</span> <span class="o">/</span> <span class="n">category</span>
-            <span class="p">)</span>
-            <span class="o">/</span> <span class="n">signal_file</span><span class="o">.</span><span class="n">name</span>
-        <span class="p">)</span>
-        <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;writing wavfile </span><span class="si">{</span><span class="n">dest</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-        <span class="n">wavfile</span><span class="o">.</span><span class="n">write</span><span class="p">(</span>
-            <span class="n">dest</span><span class="p">,</span>
-            <span class="n">sr_signal</span><span class="p">,</span>
-            <span class="p">((</span><span class="n">noised</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">noised</span><span class="p">))</span> <span class="o">*</span> <span class="n">max_sample</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">int16</span><span class="p">),</span>
-        <span class="p">)</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-    <span class="k">pass</span>
-</pre></div>
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+            <h1>Source code for modulation.signal_utilities.noise_generation.additive_noise</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 10-12-2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>from</span> <span class='nn'>pathlib</span> <span class='kn'>import</span> <span class='n'>Path</span>
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Any</span>
+
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>from</span> <span class='nn'>draugr.visualisation</span> <span class='kn'>import</span> <span class='n'>progress_bar</span>
+<span class='kn'>from</span> <span class='nn'>warg</span> <span class='kn'>import</span> <span class='n'>ensure_existence</span>
+<span class='kn'>from</span> <span class='nn'>draugr.numpy_utilities</span> <span class='kn'>import</span> <span class='n'>root_mean_square</span>
+
+<span class='kn'>from</span> <span class='nn'>scipy</span> <span class='kn'>import</span> <span class='n'>signal</span>
+<span class='kn'>from</span> <span class='nn'>scipy.io</span> <span class='kn'>import</span> <span class='n'>wavfile</span>
+
+<span class='kn'>from</span> <span class='nn'>modulation.signal_utilities</span> <span class='kn'>import</span> <span class='n'>mask_split_non_zero_concat</span>
+
+
+<div class='viewcode-block' id='sample_noise'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.noise_generation.additive_noise.sample_noise.html#modulation.signal_utilities.noise_generation.additive_noise.sample_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>sample_noise</span><span class='p'>(</span>
+    <span class='n'>noise</span><span class='p'>,</span>
+    <span class='n'>noise_rate</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span>
+    <span class='n'>signal_len</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span>
+    <span class='n'>signal_rate</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span>
+    <span class='o'>*</span><span class='p'>,</span>
+    <span class='n'>resample_noise</span><span class='p'>:</span> <span class='nb'>bool</span> <span class='o'>=</span> <span class='kc'>True</span><span class='p'>,</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Any</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param noise:</span>
+<span class='sd'>    :type noise:</span>
+<span class='sd'>    :param noise_rate:</span>
+<span class='sd'>    :type noise_rate:</span>
+<span class='sd'>    :param signal_len:</span>
+<span class='sd'>    :type signal_len:</span>
+<span class='sd'>    :param signal_rate:</span>
+<span class='sd'>    :type signal_rate:</span>
+<span class='sd'>    :param resample_noise:</span>
+<span class='sd'>    :type resample_noise:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>noise_len</span> <span class='o'>=</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>noise</span><span class='p'>)</span>
+    <span class='k'>if</span> <span class='n'>resample_noise</span><span class='p'>:</span>
+        <span class='n'>noise</span> <span class='o'>=</span> <span class='n'>signal</span><span class='o'>.</span><span class='n'>resample</span><span class='p'>(</span>
+            <span class='n'>noise</span><span class='p'>,</span> <span class='nb'>round</span><span class='p'>(</span><span class='n'>noise_len</span> <span class='o'>*</span> <span class='nb'>float</span><span class='p'>(</span><span class='n'>signal_rate</span><span class='p'>)</span> <span class='o'>/</span> <span class='n'>noise_rate</span><span class='p'>)</span>
+        <span class='p'>)</span>
+
+    <span class='n'>start_index</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>random</span><span class='o'>.</span><span class='n'>randint</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='n'>noise_len</span><span class='p'>)</span>
+    <span class='n'>noise</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>tile</span><span class='p'>(</span><span class='n'>noise</span><span class='p'>,</span> <span class='p'>(</span><span class='n'>signal_len</span> <span class='o'>//</span> <span class='n'>noise_len</span><span class='p'>)</span> <span class='o'>+</span> <span class='mi'>2</span><span class='p'>)</span>  <span class='c1'># at least tile once (=2)</span>
+    <span class='k'>return</span> <span class='n'>noise</span><span class='p'>[</span><span class='n'>start_index</span> <span class='p'>:</span> <span class='n'>signal_len</span> <span class='o'>+</span> <span class='n'>start_index</span><span class='p'>]</span></div>
+
+
+<div class='viewcode-block' id='compute_additive_noise_samples'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.noise_generation.additive_noise.compute_additive_noise_samples.html#modulation.signal_utilities.noise_generation.additive_noise.compute_additive_noise_samples'>[docs]</a><span class='k'>def</span> <span class='nf'>compute_additive_noise_samples</span><span class='p'>(</span>
+    <span class='o'>*</span><span class='p'>,</span>
+    <span class='n'>voice_activity_mask</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span>
+    <span class='n'>signal_file</span><span class='p'>:</span> <span class='n'>Path</span><span class='p'>,</span>
+    <span class='n'>category</span><span class='p'>,</span>
+    <span class='n'>out_dir</span><span class='p'>,</span>
+    <span class='n'>noise_file</span><span class='p'>,</span>
+    <span class='n'>snrs</span><span class='o'>=</span><span class='kc'>None</span><span class='p'>,</span>
+    <span class='n'>verbose</span><span class='p'>:</span> <span class='nb'>bool</span> <span class='o'>=</span> <span class='kc'>False</span><span class='p'>,</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='kc'>None</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param voice_activity_mask:</span>
+<span class='sd'>    :type voice_activity_mask:</span>
+<span class='sd'>    :param signal_file:</span>
+<span class='sd'>    :type signal_file:</span>
+<span class='sd'>    :param category:</span>
+<span class='sd'>    :type category:</span>
+<span class='sd'>    :param out_dir:</span>
+<span class='sd'>    :type out_dir:</span>
+<span class='sd'>    :param noise_file:</span>
+<span class='sd'>    :type noise_file:</span>
+<span class='sd'>    :param snrs:</span>
+<span class='sd'>    :type snrs:</span>
+<span class='sd'>    :param verbose:</span>
+<span class='sd'>    :type verbose:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>if</span> <span class='n'>snrs</span> <span class='ow'>is</span> <span class='kc'>None</span><span class='p'>:</span>
+        <span class='n'>snrs</span> <span class='o'>=</span> <span class='nb'>list</span><span class='p'>((</span><span class='n'>i</span> <span class='o'>*</span> <span class='mi'>5</span> <span class='k'>for</span> <span class='n'>i</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='mi'>5</span><span class='p'>)))</span>
+
+    <span class='n'>sr_noise</span><span class='p'>,</span> <span class='n'>noise</span> <span class='o'>=</span> <span class='n'>wavfile</span><span class='o'>.</span><span class='n'>read</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='n'>noise_file</span><span class='p'>))</span>
+    <span class='n'>sr_signal</span><span class='p'>,</span> <span class='n'>signal</span> <span class='o'>=</span> <span class='n'>wavfile</span><span class='o'>.</span><span class='n'>read</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='n'>signal_file</span><span class='p'>))</span>
+
+    <span class='n'>max_sample</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>max</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>)</span>
+    <span class='n'>signal</span> <span class='o'>=</span> <span class='n'>signal</span> <span class='o'>/</span> <span class='n'>max_sample</span>
+
+    <span class='n'>noise_part</span> <span class='o'>=</span> <span class='n'>sample_noise</span><span class='p'>(</span>
+        <span class='n'>noise</span> <span class='o'>/</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>max</span><span class='p'>(</span><span class='n'>noise</span><span class='p'>),</span>
+        <span class='n'>noise_rate</span><span class='o'>=</span><span class='n'>sr_noise</span><span class='p'>,</span>
+        <span class='n'>signal_len</span><span class='o'>=</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>),</span>
+        <span class='n'>signal_rate</span><span class='o'>=</span><span class='n'>sr_signal</span><span class='p'>,</span>
+    <span class='p'>)</span>
+
+    <span class='n'>noise_scaled</span> <span class='o'>=</span> <span class='n'>noise_part</span> <span class='o'>*</span> <span class='p'>(</span>
+        <span class='n'>root_mean_square</span><span class='p'>(</span><span class='n'>mask_split_non_zero_concat</span><span class='p'>(</span><span class='n'>voice_activity_mask</span><span class='p'>,</span> <span class='n'>signal</span><span class='p'>)[</span><span class='mi'>0</span><span class='p'>])</span>
+        <span class='o'>/</span> <span class='n'>root_mean_square</span><span class='p'>(</span><span class='n'>noise_part</span><span class='p'>)</span>
+    <span class='p'>)</span>  <span class='c1'># scaled by ratio of speech to noise level</span>
+
+    <span class='k'>for</span> <span class='n'>snr</span> <span class='ow'>in</span> <span class='n'>progress_bar</span><span class='p'>(</span><span class='n'>snrs</span><span class='p'>,</span> <span class='n'>disable</span><span class='o'>=</span><span class='ow'>not</span> <span class='n'>verbose</span><span class='p'>):</span>
+        <span class='n'>noised</span> <span class='o'>=</span> <span class='n'>signal</span> <span class='o'>+</span> <span class='n'>noise_scaled</span> <span class='o'>/</span> <span class='p'>(</span><span class='mi'>10</span> <span class='o'>**</span> <span class='p'>(</span><span class='n'>snr</span> <span class='o'>/</span> <span class='mi'>20</span><span class='p'>))</span>
+        <span class='n'>dest</span> <span class='o'>=</span> <span class='nb'>str</span><span class='p'>(</span>
+            <span class='n'>ensure_existence</span><span class='p'>(</span>
+                <span class='n'>out_dir</span> <span class='o'>/</span> <span class='sa'>f</span><span class='s1'>&#39;</span><span class='si'>{</span><span class='n'>noise_file</span><span class='o'>.</span><span class='n'>with_suffix</span><span class='p'>(</span><span class='s2'>&quot;&quot;</span><span class='p'>)</span><span class='o'>.</span><span class='n'>name</span><span class='si'>}</span><span class='s1'>_SNR_</span><span class='si'>{</span><span class='n'>snr</span><span class='si'>}</span><span class='s1'>dB&#39;</span> <span class='o'>/</span> <span class='n'>category</span>
+            <span class='p'>)</span>
+            <span class='o'>/</span> <span class='n'>signal_file</span><span class='o'>.</span><span class='n'>name</span>
+        <span class='p'>)</span>
+        <span class='k'>if</span> <span class='n'>verbose</span><span class='p'>:</span>
+            <span class='nb'>print</span><span class='p'>(</span><span class='sa'>f</span><span class='s2'>&quot;writing wavfile </span><span class='si'>{</span><span class='n'>dest</span><span class='si'>}</span><span class='s2'>&quot;</span><span class='p'>)</span>
+        <span class='n'>wavfile</span><span class='o'>.</span><span class='n'>write</span><span class='p'>(</span>
+            <span class='n'>dest</span><span class='p'>,</span>
+            <span class='n'>sr_signal</span><span class='p'>,</span>
+            <span class='p'>((</span><span class='n'>noised</span> <span class='o'>/</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>max</span><span class='p'>(</span><span class='n'>noised</span><span class='p'>))</span> <span class='o'>*</span> <span class='n'>max_sample</span><span class='p'>)</span><span class='o'>.</span><span class='n'>astype</span><span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>int16</span><span class='p'>),</span>
+        <span class='p'>)</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+    <span class='k'>pass</span>
+</pre>
+            </div>
 
           </div>
-          
+
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+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
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diff --git a/docs/github/html/_modules/modulation/signal_utilities/noise_generation/babble_noise.html b/docs/github/html/_modules/modulation/signal_utilities/noise_generation/babble_noise.html
index 08dac42..a541001 100644
--- a/docs/github/html/_modules/modulation/signal_utilities/noise_generation/babble_noise.html
+++ b/docs/github/html/_modules/modulation/signal_utilities/noise_generation/babble_noise.html
@@ -1,187 +1,181 @@
-
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+<html lang='en'>
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     <title>modulation.signal_utilities.noise_generation.babble_noise &#8212; modulation 0.0.1 documentation</title>
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-            
-  <h1>Source code for modulation.signal_utilities.noise_generation.babble_noise</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 09-12-2020</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">Sequence</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">torchaudio</span>
-<span class="kn">from</span> <span class="nn">draugr.numpy_utilities</span> <span class="kn">import</span> <span class="n">min_length_truncate_batch</span>
-<span class="kn">from</span> <span class="nn">draugr.torch_utilities</span> <span class="kn">import</span> <span class="n">to_tensor</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;generate_babble_noise&quot;</span><span class="p">]</span>
-
-
-<div class="viewcode-block" id="generate_babble_noise"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.noise_generation.babble_noise.generate_babble_noise.html#modulation.signal_utilities.noise_generation.babble_noise.generate_babble_noise">[docs]</a><span class="k">def</span> <span class="nf">generate_babble_noise</span><span class="p">(</span>
-    <span class="n">samples</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Iterable</span><span class="p">[</span><span class="n">Sequence</span><span class="p">]],</span>
-    <span class="n">sampling_rate</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">export_path</span><span class="p">:</span> <span class="n">Path</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterable</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param samples:</span>
-<span class="sd">    :type samples:</span>
-<span class="sd">    :param sampling_rate:</span>
-<span class="sd">    :type sampling_rate:</span>
-<span class="sd">    :param export_path:</span>
-<span class="sd">    :type export_path:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">samples</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">min_length_truncate_batch</span><span class="p">(</span><span class="n">samples</span><span class="p">))</span>
-    <span class="n">mixed</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">samples</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">samples</span><span class="p">)),</span> <span class="mi">0</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">export_path</span><span class="p">:</span>
-        <span class="n">torchaudio</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">export_path</span><span class="p">),</span> <span class="n">to_tensor</span><span class="p">(</span><span class="n">mixed</span><span class="p">),</span> <span class="n">sampling_rate</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">mixed</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-
-    <span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
-        <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-        <span class="kn">from</span> <span class="nn">modulation.data.audio.speech.recognition.libri_speech</span> <span class="kn">import</span> <span class="n">LibriSpeech</span>
-        <span class="kn">from</span> <span class="nn">draugr.visualisation</span> <span class="kn">import</span> <span class="n">dissected_channel_plot</span>
-
-        <span class="n">samples</span> <span class="o">=</span> <span class="mi">4</span>
-        <span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span>
-
-        <span class="n">libri_speech</span> <span class="o">=</span> <span class="n">LibriSpeech</span><span class="p">(</span>
-            <span class="n">path</span><span class="o">=</span><span class="n">Path</span><span class="o">.</span><span class="n">home</span><span class="p">()</span> <span class="o">/</span> <span class="s2">&quot;Data&quot;</span> <span class="o">/</span> <span class="s2">&quot;Audio&quot;</span> <span class="o">/</span> <span class="s2">&quot;Speech&quot;</span> <span class="o">/</span> <span class="s2">&quot;LibriSpeech&quot;</span>
-        <span class="p">)</span>
-        <span class="n">files</span><span class="p">,</span> <span class="n">sr</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span>
-            <span class="o">*</span><span class="p">[(</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">samples</span><span class="p">),</span> <span class="n">libri_speech</span><span class="p">)]</span>
-        <span class="p">)</span>
-        <span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">sr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">s</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">sr</span><span class="p">[</span><span class="mi">1</span><span class="p">:]])</span>
-        <span class="n">babble</span> <span class="o">=</span> <span class="n">generate_babble_noise</span><span class="p">(</span>
-            <span class="n">files</span><span class="p">,</span> <span class="n">sr</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">export_path</span><span class="o">=</span><span class="n">Path</span><span class="p">(</span><span class="s2">&quot;exclude&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;babble.wav&quot;</span>
-        <span class="p">)</span>
-
-        <span class="n">c_names</span> <span class="o">=</span> <span class="p">(</span><span class="o">*</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;C</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">files</span><span class="p">))),</span> <span class="s2">&quot;mixed&quot;</span><span class="p">)</span>
-        <span class="n">files</span> <span class="o">=</span> <span class="p">(</span><span class="o">*</span><span class="n">files</span><span class="p">,</span> <span class="n">babble</span><span class="p">)</span>
-        <span class="n">files</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">min_length_truncate_batch</span><span class="p">(</span><span class="n">files</span><span class="p">))</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="n">dissected_channel_plot</span><span class="p">(</span>
-            <span class="n">files</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">samples</span><span class="si">}</span><span class="s2"> sample babble&quot;</span><span class="p">,</span> <span class="n">channel_names</span><span class="o">=</span><span class="n">c_names</span>
-        <span class="p">)</span>
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-
-        <span class="n">main</span><span class="p">()</span>
-</pre></div>
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+
+            <h1>Source code for modulation.signal_utilities.noise_generation.babble_noise</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 09-12-2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>from</span> <span class='nn'>pathlib</span> <span class='kn'>import</span> <span class='n'>Path</span>
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Iterable</span><span class='p'>,</span> <span class='n'>Sequence</span>
+
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>import</span> <span class='nn'>torchaudio</span>
+<span class='kn'>from</span> <span class='nn'>draugr.numpy_utilities</span> <span class='kn'>import</span> <span class='n'>min_length_truncate_batch</span>
+<span class='kn'>from</span> <span class='nn'>draugr.torch_utilities</span> <span class='kn'>import</span> <span class='n'>to_tensor</span>
+
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[</span><span class='s2'>&quot;generate_babble_noise&quot;</span><span class='p'>]</span>
+
+
+<div class='viewcode-block' id='generate_babble_noise'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.noise_generation.babble_noise.generate_babble_noise.html#modulation.signal_utilities.noise_generation.babble_noise.generate_babble_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>generate_babble_noise</span><span class='p'>(</span>
+    <span class='n'>samples</span><span class='p'>:</span> <span class='n'>Iterable</span><span class='p'>[</span><span class='n'>Iterable</span><span class='p'>[</span><span class='n'>Sequence</span><span class='p'>]],</span>
+    <span class='n'>sampling_rate</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span>
+    <span class='o'>*</span><span class='p'>,</span>
+    <span class='n'>export_path</span><span class='p'>:</span> <span class='n'>Path</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>,</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Iterable</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param samples:</span>
+<span class='sd'>    :type samples:</span>
+<span class='sd'>    :param sampling_rate:</span>
+<span class='sd'>    :type sampling_rate:</span>
+<span class='sd'>    :param export_path:</span>
+<span class='sd'>    :type export_path:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>samples</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>(</span><span class='n'>min_length_truncate_batch</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>))</span>
+    <span class='n'>mixed</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sum</span><span class='p'>(</span><span class='n'>samples</span> <span class='o'>/</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>max</span><span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>abs</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>)),</span> <span class='mi'>0</span><span class='p'>)</span>
+    <span class='k'>if</span> <span class='n'>export_path</span><span class='p'>:</span>
+        <span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>save</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='n'>export_path</span><span class='p'>),</span> <span class='n'>to_tensor</span><span class='p'>(</span><span class='n'>mixed</span><span class='p'>),</span> <span class='n'>sampling_rate</span><span class='p'>)</span>
+    <span class='k'>return</span> <span class='n'>mixed</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+
+    <span class='k'>def</span> <span class='nf'>main</span><span class='p'>():</span>
+        <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+        <span class='kn'>from</span> <span class='nn'>modulation.data.audio.speech.recognition.libri_speech</span> <span class='kn'>import</span> <span class='n'>LibriSpeech</span>
+        <span class='kn'>from</span> <span class='nn'>draugr.visualisation</span> <span class='kn'>import</span> <span class='n'>dissected_channel_plot</span>
+
+        <span class='n'>samples</span> <span class='o'>=</span> <span class='mi'>4</span>
+        <span class='kn'>from</span> <span class='nn'>matplotlib</span> <span class='kn'>import</span> <span class='n'>pyplot</span>
+
+        <span class='n'>libri_speech</span> <span class='o'>=</span> <span class='n'>LibriSpeech</span><span class='p'>(</span>
+            <span class='n'>path</span><span class='o'>=</span><span class='n'>Path</span><span class='o'>.</span><span class='n'>home</span><span class='p'>()</span> <span class='o'>/</span> <span class='s2'>&quot;Data&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Audio&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Speech&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;LibriSpeech&quot;</span>
+        <span class='p'>)</span>
+        <span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span> <span class='o'>=</span> <span class='nb'>zip</span><span class='p'>(</span>
+            <span class='o'>*</span><span class='p'>[(</span><span class='n'>v</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span><span class='o'>.</span><span class='n'>numpy</span><span class='p'>(),</span> <span class='n'>v</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>])</span> <span class='k'>for</span> <span class='n'>_</span><span class='p'>,</span> <span class='n'>v</span> <span class='ow'>in</span> <span class='nb'>zip</span><span class='p'>(</span><span class='nb'>range</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>),</span> <span class='n'>libri_speech</span><span class='p'>)]</span>
+        <span class='p'>)</span>
+        <span class='k'>assert</span> <span class='nb'>all</span><span class='p'>([</span><span class='n'>sr</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span> <span class='o'>==</span> <span class='n'>s</span> <span class='k'>for</span> <span class='n'>s</span> <span class='ow'>in</span> <span class='n'>sr</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:]])</span>
+        <span class='n'>babble</span> <span class='o'>=</span> <span class='n'>generate_babble_noise</span><span class='p'>(</span>
+            <span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>],</span> <span class='n'>export_path</span><span class='o'>=</span><span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='s2'>&quot;babble.wav&quot;</span>
+        <span class='p'>)</span>
+
+        <span class='n'>c_names</span> <span class='o'>=</span> <span class='p'>(</span><span class='o'>*</span><span class='p'>(</span><span class='sa'>f</span><span class='s2'>&quot;C</span><span class='si'>{</span><span class='n'>i</span><span class='si'>}</span><span class='s2'>&quot;</span> <span class='k'>for</span> <span class='n'>i</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>files</span><span class='p'>))),</span> <span class='s2'>&quot;mixed&quot;</span><span class='p'>)</span>
+        <span class='n'>files</span> <span class='o'>=</span> <span class='p'>(</span><span class='o'>*</span><span class='n'>files</span><span class='p'>,</span> <span class='n'>babble</span><span class='p'>)</span>
+        <span class='n'>files</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>(</span><span class='n'>min_length_truncate_batch</span><span class='p'>(</span><span class='n'>files</span><span class='p'>))</span><span class='o'>.</span><span class='n'>squeeze</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>)</span>
+
+        <span class='n'>dissected_channel_plot</span><span class='p'>(</span>
+            <span class='n'>files</span><span class='p'>,</span> <span class='n'>title</span><span class='o'>=</span><span class='sa'>f</span><span class='s2'>&quot;</span><span class='si'>{</span><span class='n'>samples</span><span class='si'>}</span><span class='s2'> sample babble&quot;</span><span class='p'>,</span> <span class='n'>channel_names</span><span class='o'>=</span><span class='n'>c_names</span>
+        <span class='p'>)</span>
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>show</span><span class='p'>()</span>
+
+        <span class='n'>main</span><span class='p'>()</span>
+</pre>
+            </div>
 
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+++ b/docs/github/html/_modules/modulation/signal_utilities/noise_generation/gaussian_noise.html
@@ -1,314 +1,308 @@
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-  <h1>Source code for modulation.signal_utilities.noise_generation.gaussian_noise</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 10-12-2020</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
-<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
-<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">torchaudio</span>
-<span class="kn">from</span> <span class="nn">draugr.numpy_utilities</span> <span class="kn">import</span> <span class="n">get_sampler</span><span class="p">,</span> <span class="n">normalise_signal</span>
-<span class="kn">from</span> <span class="nn">draugr.torch_utilities</span> <span class="kn">import</span> <span class="n">to_tensor</span>
-<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span>
-<span class="kn">from</span> <span class="nn">numpy.fft</span> <span class="kn">import</span> <span class="n">irfft</span><span class="p">,</span> <span class="n">rfft</span>
-
-
-<div class="viewcode-block" id="white_noise"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.noise_generation.gaussian_noise.white_noise.html#modulation.signal_utilities.noise_generation.gaussian_noise.white_noise">[docs]</a><span class="k">def</span> <span class="nf">white_noise</span><span class="p">(</span><span class="n">length</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    White noise.</span>
-
-<span class="sd">    * N: Amount of samples.</span>
-
-<span class="sd">    White noise has a constant power density.</span>
-<span class="sd">    Its narrowband spectrum is therefore flat.</span>
-<span class="sd">    The power in white noise will increase by a factor of two for each octave band,</span>
-<span class="sd">    and therefore increases with 3 dB per octave.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">seed</span><span class="p">:</span>
-        <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">length</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="blue_noise"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.noise_generation.gaussian_noise.blue_noise.html#modulation.signal_utilities.noise_generation.gaussian_noise.blue_noise">[docs]</a><span class="k">def</span> <span class="nf">blue_noise</span><span class="p">(</span><span class="n">length</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Blue noise.</span>
-
-<span class="sd">    * N: Amount of samples.</span>
-
-<span class="sd">    Power increases with 6 dB per octave.</span>
-<span class="sd">    Power density increases with 3 dB per octave.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">rfft</span><span class="p">(</span><span class="n">white_noise</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">seed</span><span class="p">))</span> <span class="o">/</span> <span class="n">length</span>
-
-    <span class="k">return</span> <span class="n">normalise_signal</span><span class="p">(</span>
-        <span class="n">irfft</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">))))</span><span class="o">.</span><span class="n">real</span><span class="p">[:</span><span class="n">length</span><span class="p">]</span>
-    <span class="p">)</span>  <span class="c1"># Filter</span></div>
-
-
-<div class="viewcode-block" id="brown_noise"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.noise_generation.gaussian_noise.brown_noise.html#modulation.signal_utilities.noise_generation.gaussian_noise.brown_noise">[docs]</a><span class="k">def</span> <span class="nf">brown_noise</span><span class="p">(</span><span class="n">length</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Brown noise.</span>
-
-<span class="sd">    * N: Amount of samples.</span>
-
-<span class="sd">    Power decreases with -3 dB per octave.</span>
-<span class="sd">    Power density decreases with 6 dB per octave.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">rfft</span><span class="p">(</span><span class="n">white_noise</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">seed</span><span class="p">))</span> <span class="o">/</span> <span class="n">length</span>
-    <span class="k">return</span> <span class="n">normalise_signal</span><span class="p">(</span>
-        <span class="n">irfft</span><span class="p">(</span><span class="n">x</span> <span class="o">/</span> <span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">real</span><span class="p">[:</span><span class="n">length</span><span class="p">]</span>
-    <span class="p">)</span>  <span class="c1"># Filter</span></div>
-
-
-<div class="viewcode-block" id="pink_noise"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.noise_generation.gaussian_noise.pink_noise.html#modulation.signal_utilities.noise_generation.gaussian_noise.pink_noise">[docs]</a><span class="k">def</span> <span class="nf">pink_noise</span><span class="p">(</span><span class="n">length</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Pink noise.</span>
-
-<span class="sd">    :param length: Amount of samples.</span>
-<span class="sd">    :param seed: State of PRNG.</span>
-<span class="sd">    :type seed: :class:`numpy.random.RandomState`</span>
-
-<span class="sd">    Pink noise has equal power in bands that are proportionally wide.</span>
-<span class="sd">    Power density decreases with 3 dB per octave.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># This method uses the filter with the following coefficients.</span>
-    <span class="c1"># b = numpy.array([0.049922035, -0.095993537, 0.050612699, -0.004408786])</span>
-    <span class="c1"># a = numpy.array([1, -2.494956002, 2.017265875, -0.522189400])</span>
-    <span class="c1"># return lfilter(B, A, numpy.random.randn(N))</span>
-    <span class="c1"># Another way would be using the FFT</span>
-    <span class="c1"># x = numpy.random.randn(N)</span>
-    <span class="c1"># X = rfft(x) / N</span>
-    <span class="n">sampler</span> <span class="o">=</span> <span class="n">get_sampler</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
-    <span class="n">uneven</span> <span class="o">=</span> <span class="n">length</span> <span class="o">%</span> <span class="mi">2</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">sampler</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">length</span> <span class="o">//</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">uneven</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="n">sampler</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span>
-        <span class="n">length</span> <span class="o">//</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">uneven</span>
-    <span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="n">irfft</span><span class="p">(</span><span class="n">x</span> <span class="o">/</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> <span class="o">+</span> <span class="mf">1.0</span><span class="p">))</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">real</span>  <span class="c1"># +1 to avoid divide by zero</span>
-    <span class="k">if</span> <span class="n">uneven</span><span class="p">:</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-    <span class="k">return</span> <span class="n">normalise_signal</span><span class="p">(</span><span class="n">y</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="violet_noise"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.noise_generation.gaussian_noise.violet_noise.html#modulation.signal_utilities.noise_generation.gaussian_noise.violet_noise">[docs]</a><span class="k">def</span> <span class="nf">violet_noise</span><span class="p">(</span><span class="n">length</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Violet noise. Power increases with 6 dB per octave.</span>
-
-<span class="sd">    :param length: Amount of samples.</span>
-<span class="sd">    :param seed: State of PRNG.</span>
-<span class="sd">    :type seed: :class:`numpy.random.RandomState`</span>
-
-<span class="sd">    Power increases with +9 dB per octave.</span>
-<span class="sd">    Power density increases with +6 dB per octave.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">sampler</span> <span class="o">=</span> <span class="n">get_sampler</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
-    <span class="n">uneven</span> <span class="o">=</span> <span class="n">length</span> <span class="o">%</span> <span class="mi">2</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">sampler</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">length</span> <span class="o">//</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">uneven</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="n">sampler</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span>
-        <span class="n">length</span> <span class="o">//</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">uneven</span>
-    <span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="p">(</span><span class="n">irfft</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">))))</span><span class="o">.</span><span class="n">real</span>  <span class="c1"># Filter</span>
-    <span class="k">if</span> <span class="n">uneven</span><span class="p">:</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-    <span class="k">return</span> <span class="n">normalise_signal</span><span class="p">(</span><span class="n">y</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="GaussianNoiseTypeEnum"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.html#modulation.signal_utilities.noise_generation.gaussian_noise.GaussianNoiseTypeEnum">[docs]</a><span class="k">class</span> <span class="nc">GaussianNoiseTypeEnum</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
-    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-    <span class="n">white</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span>
-        <span class="n">white_noise</span>
-    <span class="p">)</span>  <span class="c1"># Partial to workaround interpreting as a method definition</span>
-    <span class="n">brown</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span><span class="n">brown_noise</span><span class="p">)</span>
-    <span class="n">blue</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span><span class="n">blue_noise</span><span class="p">)</span>
-    <span class="n">pink</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span><span class="n">pink_noise</span><span class="p">)</span>
-    <span class="n">violet</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span><span class="n">violet_noise</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="generate_noise"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.noise_generation.gaussian_noise.generate_noise.html#modulation.signal_utilities.noise_generation.gaussian_noise.generate_noise">[docs]</a><span class="k">def</span> <span class="nf">generate_noise</span><span class="p">(</span>
-    <span class="n">length</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">noise_type</span><span class="p">:</span> <span class="n">GaussianNoiseTypeEnum</span> <span class="o">=</span> <span class="n">GaussianNoiseTypeEnum</span><span class="o">.</span><span class="n">white</span><span class="p">,</span>
-    <span class="n">export_path</span><span class="p">:</span> <span class="n">Path</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">sampling_rate</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">16000</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param length:</span>
-<span class="sd">    :type length:</span>
-<span class="sd">    :param seed:</span>
-<span class="sd">    :type seed:</span>
-<span class="sd">    :param noise_type:</span>
-<span class="sd">    :type noise_type:</span>
-<span class="sd">    :param export_path:</span>
-<span class="sd">    :type export_path:</span>
-<span class="sd">    :param sampling_rate:</span>
-<span class="sd">    :type sampling_rate:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">normalised</span> <span class="o">=</span> <span class="n">noise_type</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">seed</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">export_path</span><span class="p">:</span>
-        <span class="n">torchaudio</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">export_path</span><span class="p">),</span> <span class="n">to_tensor</span><span class="p">(</span><span class="n">normalised</span><span class="p">),</span> <span class="n">sampling_rate</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">normalised</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-
-    <span class="k">def</span> <span class="nf">asijsda</span><span class="p">():</span>
-        <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-        <span class="n">sampling_rate</span> <span class="o">=</span> <span class="mi">16000</span>
-        <span class="n">length_sec</span> <span class="o">=</span> <span class="mi">5</span>
-        <span class="n">noises</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">GaussianNoiseTypeEnum</span><span class="p">:</span>
-            <span class="n">name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">a</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">_noise.wav&quot;</span>
-
-            <span class="n">noise</span> <span class="o">=</span> <span class="n">generate_noise</span><span class="p">(</span>
-                <span class="n">sampling_rate</span> <span class="o">*</span> <span class="n">length_sec</span><span class="p">,</span>
-                <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span>
-                <span class="n">noise_type</span><span class="o">=</span><span class="n">a</span><span class="p">,</span>
-                <span class="n">export_path</span><span class="o">=</span><span class="n">Path</span><span class="p">(</span><span class="s2">&quot;exclude&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="n">name</span><span class="p">,</span>
-            <span class="p">)</span>
-            <span class="n">noises</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">noise</span>
-
-        <span class="n">channels</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">noises</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
-        <span class="n">channels</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
-            <span class="p">[</span><span class="n">channels</span><span class="p">,</span> <span class="n">normalise_signal</span><span class="p">(</span><span class="n">channels</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">))]</span>
-        <span class="p">)</span>
-        <span class="n">channel_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">noises</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;mixed&quot;</span><span class="p">]</span>
-        <span class="kn">from</span> <span class="nn">draugr</span> <span class="kn">import</span> <span class="n">dissected_channel_plot</span>
-
-        <span class="n">dissected_channel_plot</span><span class="p">(</span>
-            <span class="n">channels</span><span class="p">,</span>
-            <span class="n">channel_names</span><span class="o">=</span><span class="n">channel_names</span><span class="p">,</span>
-            <span class="n">sampling_rate</span><span class="o">=</span><span class="n">sampling_rate</span><span class="p">,</span>
-            <span class="n">max_resolution</span><span class="o">=</span><span class="mi">16000</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-
-        <span class="n">asijsda</span><span class="p">()</span>
-</pre></div>
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+            <h1>Source code for modulation.signal_utilities.noise_generation.gaussian_noise</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 10-12-2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
 
+<span class='kn'>from</span> <span class='nn'>enum</span> <span class='kn'>import</span> <span class='n'>Enum</span>
+<span class='kn'>from</span> <span class='nn'>functools</span> <span class='kn'>import</span> <span class='n'>partial</span>
+<span class='kn'>from</span> <span class='nn'>pathlib</span> <span class='kn'>import</span> <span class='n'>Path</span>
 
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>import</span> <span class='nn'>torchaudio</span>
+<span class='kn'>from</span> <span class='nn'>draugr.numpy_utilities</span> <span class='kn'>import</span> <span class='n'>get_sampler</span><span class='p'>,</span> <span class='n'>normalise_signal</span>
+<span class='kn'>from</span> <span class='nn'>draugr.torch_utilities</span> <span class='kn'>import</span> <span class='n'>to_tensor</span>
+<span class='kn'>from</span> <span class='nn'>matplotlib</span> <span class='kn'>import</span> <span class='n'>pyplot</span>
+<span class='kn'>from</span> <span class='nn'>numpy.fft</span> <span class='kn'>import</span> <span class='n'>irfft</span><span class='p'>,</span> <span class='n'>rfft</span>
 
 
+<div class='viewcode-block' id='white_noise'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.noise_generation.gaussian_noise.white_noise.html#modulation.signal_utilities.noise_generation.gaussian_noise.white_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>white_noise</span><span class='p'>(</span><span class='n'>length</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    White noise.</span>
 
+<span class='sd'>    * N: Amount of samples.</span>
 
+<span class='sd'>    White noise has a constant power density.</span>
+<span class='sd'>    Its narrowband spectrum is therefore flat.</span>
+<span class='sd'>    The power in white noise will increase by a factor of two for each octave band,</span>
+<span class='sd'>    and therefore increases with 3 dB per octave.</span>
 
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>if</span> <span class='n'>seed</span><span class='p'>:</span>
+        <span class='n'>numpy</span><span class='o'>.</span><span class='n'>random</span><span class='o'>.</span><span class='n'>seed</span><span class='p'>(</span><span class='n'>seed</span><span class='p'>)</span>
+    <span class='k'>return</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>random</span><span class='o'>.</span><span class='n'>randn</span><span class='p'>(</span><span class='n'>length</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='blue_noise'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.noise_generation.gaussian_noise.blue_noise.html#modulation.signal_utilities.noise_generation.gaussian_noise.blue_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>blue_noise</span><span class='p'>(</span><span class='n'>length</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    Blue noise.</span>
+
+<span class='sd'>    * N: Amount of samples.</span>
+
+<span class='sd'>    Power increases with 6 dB per octave.</span>
+<span class='sd'>    Power density increases with 3 dB per octave.</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>x</span> <span class='o'>=</span> <span class='n'>rfft</span><span class='p'>(</span><span class='n'>white_noise</span><span class='p'>(</span><span class='n'>length</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>))</span> <span class='o'>/</span> <span class='n'>length</span>
+
+    <span class='k'>return</span> <span class='n'>normalise_signal</span><span class='p'>(</span>
+        <span class='n'>irfft</span><span class='p'>(</span><span class='n'>x</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sqrt</span><span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>arange</span><span class='p'>(</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>x</span><span class='p'>))))</span><span class='o'>.</span><span class='n'>real</span><span class='p'>[:</span><span class='n'>length</span><span class='p'>]</span>
+    <span class='p'>)</span>  <span class='c1'># Filter</span></div>
+
+
+<div class='viewcode-block' id='brown_noise'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.noise_generation.gaussian_noise.brown_noise.html#modulation.signal_utilities.noise_generation.gaussian_noise.brown_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>brown_noise</span><span class='p'>(</span><span class='n'>length</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    Brown noise.</span>
+
+<span class='sd'>    * N: Amount of samples.</span>
+
+<span class='sd'>    Power decreases with -3 dB per octave.</span>
+<span class='sd'>    Power density decreases with 6 dB per octave.</span>
+
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>x</span> <span class='o'>=</span> <span class='n'>rfft</span><span class='p'>(</span><span class='n'>white_noise</span><span class='p'>(</span><span class='n'>length</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>))</span> <span class='o'>/</span> <span class='n'>length</span>
+    <span class='k'>return</span> <span class='n'>normalise_signal</span><span class='p'>(</span>
+        <span class='n'>irfft</span><span class='p'>(</span><span class='n'>x</span> <span class='o'>/</span> <span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>arange</span><span class='p'>(</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>x</span><span class='p'>))</span> <span class='o'>+</span> <span class='mi'>1</span><span class='p'>))</span><span class='o'>.</span><span class='n'>real</span><span class='p'>[:</span><span class='n'>length</span><span class='p'>]</span>
+    <span class='p'>)</span>  <span class='c1'># Filter</span></div>
+
+
+<div class='viewcode-block' id='pink_noise'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.noise_generation.gaussian_noise.pink_noise.html#modulation.signal_utilities.noise_generation.gaussian_noise.pink_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>pink_noise</span><span class='p'>(</span><span class='n'>length</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    Pink noise.</span>
+
+<span class='sd'>    :param length: Amount of samples.</span>
+<span class='sd'>    :param seed: State of PRNG.</span>
+<span class='sd'>    :type seed: :class:`numpy.random.RandomState`</span>
+
+<span class='sd'>    Pink noise has equal power in bands that are proportionally wide.</span>
+<span class='sd'>    Power density decreases with 3 dB per octave.</span>
+
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='c1'># This method uses the filter with the following coefficients.</span>
+    <span class='c1'># b = numpy.array([0.049922035, -0.095993537, 0.050612699, -0.004408786])</span>
+    <span class='c1'># a = numpy.array([1, -2.494956002, 2.017265875, -0.522189400])</span>
+    <span class='c1'># return lfilter(B, A, numpy.random.randn(N))</span>
+    <span class='c1'># Another way would be using the FFT</span>
+    <span class='c1'># x = numpy.random.randn(N)</span>
+    <span class='c1'># X = rfft(x) / N</span>
+    <span class='n'>sampler</span> <span class='o'>=</span> <span class='n'>get_sampler</span><span class='p'>(</span><span class='n'>seed</span><span class='p'>)</span>
+    <span class='n'>uneven</span> <span class='o'>=</span> <span class='n'>length</span> <span class='o'>%</span> <span class='mi'>2</span>
+    <span class='n'>x</span> <span class='o'>=</span> <span class='n'>sampler</span><span class='o'>.</span><span class='n'>randn</span><span class='p'>(</span><span class='n'>length</span> <span class='o'>//</span> <span class='mi'>2</span> <span class='o'>+</span> <span class='mi'>1</span> <span class='o'>+</span> <span class='n'>uneven</span><span class='p'>)</span> <span class='o'>+</span> <span class='mi'>1</span><span class='n'>j</span> <span class='o'>*</span> <span class='n'>sampler</span><span class='o'>.</span><span class='n'>randn</span><span class='p'>(</span>
+        <span class='n'>length</span> <span class='o'>//</span> <span class='mi'>2</span> <span class='o'>+</span> <span class='mi'>1</span> <span class='o'>+</span> <span class='n'>uneven</span>
+    <span class='p'>)</span>
+    <span class='n'>y</span> <span class='o'>=</span> <span class='p'>(</span>
+        <span class='n'>irfft</span><span class='p'>(</span><span class='n'>x</span> <span class='o'>/</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sqrt</span><span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>arange</span><span class='p'>(</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>x</span><span class='p'>))</span> <span class='o'>+</span> <span class='mf'>1.0</span><span class='p'>))</span>
+    <span class='p'>)</span><span class='o'>.</span><span class='n'>real</span>  <span class='c1'># +1 to avoid divide by zero</span>
+    <span class='k'>if</span> <span class='n'>uneven</span><span class='p'>:</span>
+        <span class='n'>y</span> <span class='o'>=</span> <span class='n'>y</span><span class='p'>[:</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>]</span>
+    <span class='k'>return</span> <span class='n'>normalise_signal</span><span class='p'>(</span><span class='n'>y</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='violet_noise'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.noise_generation.gaussian_noise.violet_noise.html#modulation.signal_utilities.noise_generation.gaussian_noise.violet_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>violet_noise</span><span class='p'>(</span><span class='n'>length</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    Violet noise. Power increases with 6 dB per octave.</span>
+
+<span class='sd'>    :param length: Amount of samples.</span>
+<span class='sd'>    :param seed: State of PRNG.</span>
+<span class='sd'>    :type seed: :class:`numpy.random.RandomState`</span>
+
+<span class='sd'>    Power increases with +9 dB per octave.</span>
+<span class='sd'>    Power density increases with +6 dB per octave.</span>
+
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>sampler</span> <span class='o'>=</span> <span class='n'>get_sampler</span><span class='p'>(</span><span class='n'>seed</span><span class='p'>)</span>
+    <span class='n'>uneven</span> <span class='o'>=</span> <span class='n'>length</span> <span class='o'>%</span> <span class='mi'>2</span>
+    <span class='n'>x</span> <span class='o'>=</span> <span class='n'>sampler</span><span class='o'>.</span><span class='n'>randn</span><span class='p'>(</span><span class='n'>length</span> <span class='o'>//</span> <span class='mi'>2</span> <span class='o'>+</span> <span class='mi'>1</span> <span class='o'>+</span> <span class='n'>uneven</span><span class='p'>)</span> <span class='o'>+</span> <span class='mi'>1</span><span class='n'>j</span> <span class='o'>*</span> <span class='n'>sampler</span><span class='o'>.</span><span class='n'>randn</span><span class='p'>(</span>
+        <span class='n'>length</span> <span class='o'>//</span> <span class='mi'>2</span> <span class='o'>+</span> <span class='mi'>1</span> <span class='o'>+</span> <span class='n'>uneven</span>
+    <span class='p'>)</span>
+    <span class='n'>y</span> <span class='o'>=</span> <span class='p'>(</span><span class='n'>irfft</span><span class='p'>(</span><span class='n'>x</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>arange</span><span class='p'>(</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>x</span><span class='p'>))))</span><span class='o'>.</span><span class='n'>real</span>  <span class='c1'># Filter</span>
+    <span class='k'>if</span> <span class='n'>uneven</span><span class='p'>:</span>
+        <span class='n'>y</span> <span class='o'>=</span> <span class='n'>y</span><span class='p'>[:</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>]</span>
+    <span class='k'>return</span> <span class='n'>normalise_signal</span><span class='p'>(</span><span class='n'>y</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='GaussianNoiseTypeEnum'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.html#modulation.signal_utilities.noise_generation.gaussian_noise.GaussianNoiseTypeEnum'>[docs]</a><span class='k'>class</span> <span class='nc'>GaussianNoiseTypeEnum</span><span class='p'>(</span><span class='n'>Enum</span><span class='p'>):</span>
+    <span class='sa'>r</span><span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+    <span class='n'>white</span> <span class='o'>=</span> <span class='n'>partial</span><span class='p'>(</span>
+        <span class='n'>white_noise</span>
+    <span class='p'>)</span>  <span class='c1'># Partial to workaround interpreting as a method definition</span>
+    <span class='n'>brown</span> <span class='o'>=</span> <span class='n'>partial</span><span class='p'>(</span><span class='n'>brown_noise</span><span class='p'>)</span>
+    <span class='n'>blue</span> <span class='o'>=</span> <span class='n'>partial</span><span class='p'>(</span><span class='n'>blue_noise</span><span class='p'>)</span>
+    <span class='n'>pink</span> <span class='o'>=</span> <span class='n'>partial</span><span class='p'>(</span><span class='n'>pink_noise</span><span class='p'>)</span>
+    <span class='n'>violet</span> <span class='o'>=</span> <span class='n'>partial</span><span class='p'>(</span><span class='n'>violet_noise</span><span class='p'>)</span>
+
+    <span class='k'>def</span> <span class='fm'>__call__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='o'>*</span><span class='n'>args</span><span class='p'>,</span> <span class='o'>**</span><span class='n'>kwargs</span><span class='p'>):</span>
+        <span class='k'>return</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>value</span><span class='p'>(</span><span class='o'>*</span><span class='n'>args</span><span class='p'>,</span> <span class='o'>**</span><span class='n'>kwargs</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='generate_noise'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.noise_generation.gaussian_noise.generate_noise.html#modulation.signal_utilities.noise_generation.gaussian_noise.generate_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>generate_noise</span><span class='p'>(</span>
+    <span class='n'>length</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span>
+    <span class='o'>*</span><span class='p'>,</span>
+    <span class='n'>seed</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>,</span>
+    <span class='n'>noise_type</span><span class='p'>:</span> <span class='n'>GaussianNoiseTypeEnum</span> <span class='o'>=</span> <span class='n'>GaussianNoiseTypeEnum</span><span class='o'>.</span><span class='n'>white</span><span class='p'>,</span>
+    <span class='n'>export_path</span><span class='p'>:</span> <span class='n'>Path</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>,</span>
+    <span class='n'>sampling_rate</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='mi'>16000</span><span class='p'>,</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param length:</span>
+<span class='sd'>    :type length:</span>
+<span class='sd'>    :param seed:</span>
+<span class='sd'>    :type seed:</span>
+<span class='sd'>    :param noise_type:</span>
+<span class='sd'>    :type noise_type:</span>
+<span class='sd'>    :param export_path:</span>
+<span class='sd'>    :type export_path:</span>
+<span class='sd'>    :param sampling_rate:</span>
+<span class='sd'>    :type sampling_rate:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>normalised</span> <span class='o'>=</span> <span class='n'>noise_type</span><span class='p'>(</span><span class='n'>length</span><span class='p'>,</span> <span class='n'>seed</span><span class='p'>)</span>
+    <span class='k'>if</span> <span class='n'>export_path</span><span class='p'>:</span>
+        <span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>save</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='n'>export_path</span><span class='p'>),</span> <span class='n'>to_tensor</span><span class='p'>(</span><span class='n'>normalised</span><span class='p'>),</span> <span class='n'>sampling_rate</span><span class='p'>)</span>
+    <span class='k'>return</span> <span class='n'>normalised</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+
+    <span class='k'>def</span> <span class='nf'>asijsda</span><span class='p'>():</span>
+        <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+        <span class='n'>sampling_rate</span> <span class='o'>=</span> <span class='mi'>16000</span>
+        <span class='n'>length_sec</span> <span class='o'>=</span> <span class='mi'>5</span>
+        <span class='n'>noises</span> <span class='o'>=</span> <span class='p'>{}</span>
+        <span class='k'>for</span> <span class='n'>a</span> <span class='ow'>in</span> <span class='n'>GaussianNoiseTypeEnum</span><span class='p'>:</span>
+            <span class='n'>name</span> <span class='o'>=</span> <span class='sa'>f</span><span class='s2'>&quot;</span><span class='si'>{</span><span class='n'>a</span><span class='o'>.</span><span class='n'>name</span><span class='si'>}</span><span class='s2'>_noise.wav&quot;</span>
+
+            <span class='n'>noise</span> <span class='o'>=</span> <span class='n'>generate_noise</span><span class='p'>(</span>
+                <span class='n'>sampling_rate</span> <span class='o'>*</span> <span class='n'>length_sec</span><span class='p'>,</span>
+                <span class='n'>seed</span><span class='o'>=</span><span class='mi'>42</span><span class='p'>,</span>
+                <span class='n'>noise_type</span><span class='o'>=</span><span class='n'>a</span><span class='p'>,</span>
+                <span class='n'>export_path</span><span class='o'>=</span><span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='n'>name</span><span class='p'>,</span>
+            <span class='p'>)</span>
+            <span class='n'>noises</span><span class='p'>[</span><span class='n'>name</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>noise</span>
+
+        <span class='n'>channels</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>(</span><span class='nb'>list</span><span class='p'>(</span><span class='n'>noises</span><span class='o'>.</span><span class='n'>values</span><span class='p'>()))</span>
+        <span class='n'>channels</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>concatenate</span><span class='p'>(</span>
+            <span class='p'>[</span><span class='n'>channels</span><span class='p'>,</span> <span class='n'>normalise_signal</span><span class='p'>(</span><span class='n'>channels</span><span class='o'>.</span><span class='n'>sum</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='n'>keepdims</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>))]</span>
+        <span class='p'>)</span>
+        <span class='n'>channel_names</span> <span class='o'>=</span> <span class='nb'>list</span><span class='p'>(</span><span class='n'>noises</span><span class='o'>.</span><span class='n'>keys</span><span class='p'>())</span> <span class='o'>+</span> <span class='p'>[</span><span class='s2'>&quot;mixed&quot;</span><span class='p'>]</span>
+        <span class='kn'>from</span> <span class='nn'>draugr</span> <span class='kn'>import</span> <span class='n'>dissected_channel_plot</span>
+
+        <span class='n'>dissected_channel_plot</span><span class='p'>(</span>
+            <span class='n'>channels</span><span class='p'>,</span>
+            <span class='n'>channel_names</span><span class='o'>=</span><span class='n'>channel_names</span><span class='p'>,</span>
+            <span class='n'>sampling_rate</span><span class='o'>=</span><span class='n'>sampling_rate</span><span class='p'>,</span>
+            <span class='n'>max_resolution</span><span class='o'>=</span><span class='mi'>16000</span><span class='p'>,</span>
+        <span class='p'>)</span>
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>show</span><span class='p'>()</span>
+
+        <span class='n'>asijsda</span><span class='p'>()</span>
+</pre>
+            </div>
+
+          </div>
 
         </div>
       </div>
-      <div class="clearer"></div>
+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <p class='logo'><a href='../../../../index.html'>
+            <img alt='Logo' class='logo' src='../../../../_static/mod.svg'/>
+          </a></p>
+          <h1 class='logo'><a href='../../../../index.html'>modulation</a></h1>
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+          <h3>Navigation</h3>
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../../getting_started.html'>Getting Started</a></li>
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+
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+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../../../../index.html'>Documentation overview</a>
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index bad3eb7..ef26f58 100644
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+++ b/docs/github/html/_modules/modulation/signal_utilities/noise_generation/shaped_noise.html
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     <title>modulation.signal_utilities.noise_generation.shaped_noise &#8212; modulation 0.0.1 documentation</title>
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-            
-  <h1>Source code for modulation.signal_utilities.noise_generation.shaped_noise</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 09-12-2020</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">torchaudio</span>
-<span class="kn">from</span> <span class="nn">draugr.numpy_utilities</span> <span class="kn">import</span> <span class="n">zero_pad_to_power_2</span>
-<span class="kn">from</span> <span class="nn">draugr.random_utilities.seeding</span> <span class="kn">import</span> <span class="n">numpy_seed</span>
-<span class="kn">from</span> <span class="nn">draugr.torch_utilities</span> <span class="kn">import</span> <span class="n">to_tensor</span>
-<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span>
-<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">fft</span>
-<span class="kn">from</span> <span class="nn">scipy.signal</span> <span class="kn">import</span> <span class="n">butter</span><span class="p">,</span> <span class="n">filtfilt</span><span class="p">,</span> <span class="n">welch</span>
-<span class="kn">from</span> <span class="nn">warg</span> <span class="kn">import</span> <span class="n">next_pow_2</span>
-
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;generate_speech_shaped_noise&quot;</span><span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">modulation.signal_utilities.audio_utilities.filtering</span> <span class="kn">import</span> <span class="n">hilbert_envelope</span>
-
-
-<span class="k">def</span> <span class="nf">spectrum_like_noise</span><span class="p">(</span>
-    <span class="n">signal</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">sampling_rate</span><span class="o">=</span><span class="mi">40000</span><span class="p">,</span>
-    <span class="n">keep_signal_amp_envelope</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">low_pass_cutoff</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>  <span class="c1"># Hz</span>
-    <span class="n">low_pass_order</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
-    <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">42</span><span class="p">,</span>
-    <span class="n">window_length_sec</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">20</span> <span class="o">/</span> <span class="mi">1000</span><span class="p">,</span>  <span class="c1"># 20 ms</span>
-    <span class="n">p_overlap</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span>
-    <span class="n">long_term_avg</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;Create a noise with same spectrum as the input signal.</span>
-<span class="sd">    randomises phase</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    signal : array_like</span>
-<span class="sd">        Input signal.</span>
-<span class="sd">    sampling_rate : int</span>
-<span class="sd">         Sampling frequency of the input signal. (Default value = 40000)</span>
-<span class="sd">    keep_signal_amp_envelope : bool</span>
-<span class="sd">         Apply the envelope of the original signal to the noise. (Default</span>
-<span class="sd">         value = False)</span>
-<span class="sd">    low_pass_cutoff : float</span>
-<span class="sd">    low_pass_order : int</span>
-<span class="sd">    seed : int</span>
-<span class="sd">    long_term_avg : bool</span>
-<span class="sd">    window_length_sec: int</span>
-<span class="sd">    p_overlap: float</span>
-
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    ndarray</span>
-<span class="sd">        Noise signal.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">assert</span> <span class="n">window_length_sec</span> <span class="o">&gt;</span> <span class="mi">0</span>
-    <span class="k">assert</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">p_overlap</span> <span class="o">&lt;=</span> <span class="mi">1</span>
-    <span class="n">signal</span> <span class="o">=</span> <span class="n">zero_pad_to_power_2</span><span class="p">(</span><span class="n">signal</span><span class="p">)</span>  <span class="c1"># Ensure welch works with any window size</span>
-    <span class="n">signal_length</span> <span class="o">=</span> <span class="n">signal</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-    <span class="n">window_sum_squares</span> <span class="o">=</span> <span class="n">signal_length</span>  <span class="c1"># scaling factor defined as sum of squared samples of window function</span>
-    <span class="n">sc</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">/</span> <span class="p">(</span>
-        <span class="n">sampling_rate</span> <span class="o">*</span> <span class="n">window_sum_squares</span>
-    <span class="p">)</span>  <span class="c1"># Scaling coefficient 2 takes into account removal of energy at negative frequencies (we drop this side of PSD)</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">long_term_avg</span><span class="p">:</span>
-        <span class="n">n_fft</span> <span class="o">=</span> <span class="n">next_pow_2</span><span class="p">(</span><span class="n">signal_length</span><span class="p">)</span>
-        <span class="n">spec</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">fft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">n_fft</span><span class="p">))</span>
-        <span class="n">psd</span> <span class="o">=</span> <span class="p">(</span><span class="n">spec</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">sc</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">n_per_seg</span> <span class="o">=</span> <span class="n">next_pow_2</span><span class="p">(</span>
-            <span class="nb">int</span><span class="p">(</span><span class="n">sampling_rate</span> <span class="o">*</span> <span class="n">window_length_sec</span><span class="p">)</span>
-        <span class="p">)</span>  <span class="c1"># next_pow_2 per seg == n_fft</span>
-        <span class="n">n_overlap</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n_per_seg</span> <span class="o">*</span> <span class="n">p_overlap</span><span class="p">)</span>
-        <span class="n">f</span><span class="p">,</span> <span class="n">psd</span> <span class="o">=</span> <span class="n">welch</span><span class="p">(</span>
-            <span class="n">signal</span><span class="p">,</span>
-            <span class="n">sampling_rate</span><span class="p">,</span>
-            <span class="n">nperseg</span><span class="o">=</span><span class="n">n_per_seg</span><span class="p">,</span>
-            <span class="n">noverlap</span><span class="o">=</span><span class="n">n_overlap</span><span class="p">,</span>
-            <span class="n">scaling</span><span class="o">=</span><span class="s2">&quot;density&quot;</span><span class="p">,</span>
-            <span class="n">return_onesided</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="n">detrend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-            <span class="c1"># window=&#39;boxcar&#39;,</span>
-            <span class="n">window</span><span class="o">=</span><span class="s2">&quot;hanning&quot;</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">n_fft</span> <span class="o">=</span> <span class="n">n_per_seg</span>
-        <span class="n">psd</span> <span class="o">/=</span> <span class="n">signal_length</span> <span class="o">/</span> <span class="n">n_per_seg</span>  <span class="c1"># normalise?</span>
-    <span class="n">spec</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">psd</span> <span class="o">/</span> <span class="n">sc</span><span class="p">))</span>
-
-    <span class="n">noise</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">runs</span> <span class="o">=</span> <span class="n">signal_length</span> <span class="o">//</span> <span class="n">n_fft</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">runs</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-        <span class="n">numpy_seed</span><span class="p">(</span><span class="n">seed</span> <span class="o">+</span> <span class="n">i</span><span class="p">)</span>
-        <span class="n">noise</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
-            <span class="n">numpy</span><span class="o">.</span><span class="n">real</span><span class="p">(</span>
-                <span class="n">fft</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span>
-                    <span class="n">spec</span>
-                    <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span>
-                        <span class="mi">2</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="n">spec</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-                    <span class="p">),</span>  <span class="c1"># Randomise phase. 0-&gt;360, 2 pi rads</span>
-                    <span class="n">n_fft</span><span class="p">,</span>
-                <span class="p">)</span>
-            <span class="p">)</span>
-        <span class="p">)</span>  <span class="c1"># Give each spectral component a random phase, PHI(f(k)) = random number,</span>
-        <span class="c1"># uniformly distributed between 0 and 360 degrees (or equivalently, between 0 and 2Pi radians);</span>
-
-    <span class="n">noise</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">noise</span><span class="p">)[:</span><span class="n">signal_length</span><span class="p">]</span>
-
-    <span class="k">if</span> <span class="n">keep_signal_amp_envelope</span><span class="p">:</span>
-        <span class="p">[</span><span class="n">bb</span><span class="p">,</span> <span class="n">aa</span><span class="p">]</span> <span class="o">=</span> <span class="n">butter</span><span class="p">(</span>
-            <span class="n">low_pass_order</span><span class="p">,</span> <span class="n">low_pass_cutoff</span> <span class="o">/</span> <span class="p">(</span><span class="n">sampling_rate</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
-        <span class="p">)</span>  <span class="c1"># Cutoff Hz, LP filter</span>
-        <span class="n">noise</span> <span class="o">*=</span> <span class="n">filtfilt</span><span class="p">(</span>
-            <span class="n">bb</span><span class="p">,</span>  <span class="c1"># numerator</span>
-            <span class="n">aa</span><span class="p">,</span>  <span class="c1"># denominator</span>
-            <span class="n">hilbert_envelope</span><span class="p">(</span><span class="n">signal</span><span class="p">),</span>  <span class="c1"># envelope of speech signal in time domain</span>
-        <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">noise</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="generate_speech_shaped_noise"><a class="viewcode-back" href="../../../../generated/modulation.signal_utilities.noise_generation.shaped_noise.generate_speech_shaped_noise.html#modulation.signal_utilities.noise_generation.shaped_noise.generate_speech_shaped_noise">[docs]</a><span class="k">def</span> <span class="nf">generate_speech_shaped_noise</span><span class="p">(</span>
-    <span class="n">samples</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">],</span>
-    <span class="n">sampling_rate</span><span class="p">,</span>
-    <span class="o">*</span><span class="p">,</span>
-    <span class="n">export_path</span><span class="p">:</span> <span class="n">Path</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">long_term_avg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">window_length_sec</span><span class="o">=</span><span class="mi">20</span> <span class="o">/</span> <span class="mi">1000</span><span class="p">,</span>
-    <span class="n">p_overlap</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterable</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    White (flat-spectrum) noise has more energy at high frequencies, and therefore produces more masking of the higher formants and frication noises than speech-shaped noise, speech shaped noise mask evenly throughout the speech signal spectrum</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">samples</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">noise</span> <span class="o">=</span> <span class="n">spectrum_like_noise</span><span class="p">(</span>
-        <span class="n">samples</span><span class="p">,</span>
-        <span class="n">sampling_rate</span><span class="o">=</span><span class="n">sampling_rate</span><span class="p">,</span>
-        <span class="n">long_term_avg</span><span class="o">=</span><span class="n">long_term_avg</span><span class="p">,</span>
-        <span class="n">window_length_sec</span><span class="o">=</span><span class="n">window_length_sec</span><span class="p">,</span>
-        <span class="n">p_overlap</span><span class="o">=</span><span class="n">p_overlap</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="k">if</span> <span class="n">export_path</span><span class="p">:</span>
-        <span class="n">torchaudio</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">export_path</span><span class="p">),</span> <span class="n">to_tensor</span><span class="p">(</span><span class="n">noise</span><span class="p">),</span> <span class="n">sampling_rate</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">noise</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-
-    <span class="k">def</span> <span class="nf">reald</span><span class="p">():</span>
-        <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-        <span class="kn">from</span> <span class="nn">draugr.visualisation</span> <span class="kn">import</span> <span class="n">ltass_plot</span>
-        <span class="kn">from</span> <span class="nn">modulation.data.audio.speech.recognition.libri_speech</span> <span class="kn">import</span> <span class="n">LibriSpeech</span>
-
-        <span class="n">libri_speech</span> <span class="o">=</span> <span class="n">LibriSpeech</span><span class="p">(</span>
-            <span class="n">path</span><span class="o">=</span><span class="n">Path</span><span class="o">.</span><span class="n">home</span><span class="p">()</span> <span class="o">/</span> <span class="s2">&quot;Data&quot;</span> <span class="o">/</span> <span class="s2">&quot;Audio&quot;</span> <span class="o">/</span> <span class="s2">&quot;Speech&quot;</span> <span class="o">/</span> <span class="s2">&quot;LibriSpeech&quot;</span>
-        <span class="p">)</span>
-        <span class="n">files</span><span class="p">,</span> <span class="n">sr</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span>
-            <span class="o">*</span><span class="p">[(</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">20</span><span class="p">),</span> <span class="n">libri_speech</span><span class="p">)]</span>
-        <span class="p">)</span>
-        <span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">sr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">s</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">sr</span><span class="p">[</span><span class="mi">1</span><span class="p">:]])</span>
-        <span class="n">sr</span> <span class="o">=</span> <span class="n">sr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="c1"># from modulation.noise_generation import generate_babble_noise</span>
-        <span class="c1"># files = generate_babble_noise(samples, sampling_rate)</span>
-        <span class="n">samples_len</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">files</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
-
-        <span class="n">noise_welch</span> <span class="o">=</span> <span class="n">generate_speech_shaped_noise</span><span class="p">(</span>
-            <span class="n">files</span><span class="p">,</span> <span class="n">sr</span><span class="p">,</span> <span class="n">long_term_avg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">export_path</span><span class="o">=</span><span class="n">Path</span><span class="p">(</span><span class="s2">&quot;exclude&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;ssn_welch.wav&quot;</span>
-        <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">noise_fft</span> <span class="o">=</span> <span class="n">generate_speech_shaped_noise</span><span class="p">(</span>
-            <span class="n">files</span><span class="p">,</span> <span class="n">sr</span><span class="p">,</span> <span class="n">long_term_avg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">export_path</span><span class="o">=</span><span class="n">Path</span><span class="p">(</span><span class="s2">&quot;exclude&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;ssn_fft.wav&quot;</span>
-        <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="c1"># noise_welch_full = generate_speech_shaped_noise(files, sr, long_term_avg=True, window_length_sec= samples_len/sr,p_overlap=0)[0]</span>
-        <span class="n">files</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">files</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">torchaudio</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="s2">&quot;exclude&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;ssn_signal.wav&quot;</span><span class="p">),</span> <span class="n">to_tensor</span><span class="p">(</span><span class="n">files</span><span class="p">),</span> <span class="n">sr</span><span class="p">)</span>
-
-        <span class="n">ltass_plot</span><span class="p">(</span><span class="n">files</span><span class="p">,</span> <span class="n">sr</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;signal&quot;</span><span class="p">)</span>
-        <span class="n">ltass_plot</span><span class="p">(</span><span class="n">noise_welch</span><span class="p">,</span> <span class="n">sr</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;noise_welch&quot;</span><span class="p">)</span>
-        <span class="c1"># ltass_plot(noise_welch_full, sr, label=&#39;noise_welch_full&#39;)</span>
-        <span class="n">ltass_plot</span><span class="p">(</span><span class="n">noise_fft</span><span class="p">,</span> <span class="n">sr</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;noise_fft&quot;</span><span class="p">)</span>
-
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-
-    <span class="k">def</span> <span class="nf">synth</span><span class="p">():</span>
-        <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-        <span class="kn">from</span> <span class="nn">draugr.visualisation</span> <span class="kn">import</span> <span class="n">ltass_plot</span>
-        <span class="kn">from</span> <span class="nn">modulation.data.audio.speech.recognition.libri_speech</span> <span class="kn">import</span> <span class="n">LibriSpeech</span>
-
-        <span class="n">signal_length_sec</span> <span class="o">=</span> <span class="mi">11</span>
-        <span class="n">t</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span>
-            <span class="mi">0</span><span class="p">,</span> <span class="n">signal_length_sec</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="mi">1000</span> <span class="o">*</span> <span class="n">signal_length_sec</span><span class="p">),</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">False</span>
-        <span class="p">)</span>
-        <span class="n">signal</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="n">numpy</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">50</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">t</span><span class="p">)</span>
-            <span class="o">+</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">200</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">t</span><span class="p">)</span>
-            <span class="o">+</span> <span class="n">numpy</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">400</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">numpy</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">t</span><span class="p">)</span>
-        <span class="p">)</span>
-        <span class="n">sr</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="o">/</span> <span class="n">signal_length_sec</span>
-
-        <span class="n">noise_welch</span> <span class="o">=</span> <span class="n">spectrum_like_noise</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">sampling_rate</span><span class="o">=</span><span class="n">sr</span><span class="p">,</span> <span class="n">long_term_avg</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span>
-            <span class="mi">0</span>
-        <span class="p">]</span>
-        <span class="n">noise_welch_full</span> <span class="o">=</span> <span class="n">spectrum_like_noise</span><span class="p">(</span>
-            <span class="n">signal</span><span class="p">,</span>
-            <span class="n">sampling_rate</span><span class="o">=</span><span class="n">sr</span><span class="p">,</span>
-            <span class="n">long_term_avg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="n">window_length_sec</span><span class="o">=</span><span class="n">signal_length_sec</span><span class="p">,</span>
-            <span class="n">p_overlap</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-        <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">noise_fft</span> <span class="o">=</span> <span class="n">spectrum_like_noise</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">sampling_rate</span><span class="o">=</span><span class="n">sr</span><span class="p">,</span> <span class="n">long_term_avg</span><span class="o">=</span><span class="kc">False</span><span class="p">)[</span>
-            <span class="mi">0</span>
-        <span class="p">]</span>
-
-        <span class="n">ltass_plot</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">sr</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;signal&quot;</span><span class="p">)</span>
-        <span class="n">ltass_plot</span><span class="p">(</span><span class="n">noise_welch</span><span class="p">,</span> <span class="n">sr</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;noise_welch&quot;</span><span class="p">)</span>
-        <span class="n">ltass_plot</span><span class="p">(</span><span class="n">noise_welch_full</span><span class="p">,</span> <span class="n">sr</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;noise_welch_full&quot;</span><span class="p">)</span>
-        <span class="n">ltass_plot</span><span class="p">(</span><span class="n">noise_fft</span><span class="p">,</span> <span class="n">sr</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;noise_fft&quot;</span><span class="p">)</span>
-
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-
-    <span class="k">def</span> <span class="nf">distinct_real</span><span class="p">():</span>
-        <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-        <span class="kn">from</span> <span class="nn">draugr.visualisation</span> <span class="kn">import</span> <span class="n">ltass_plot</span>
-        <span class="kn">from</span> <span class="nn">modulation.data.audio.speech.recognition.libri_speech</span> <span class="kn">import</span> <span class="n">LibriSpeech</span>
-
-        <span class="n">samples</span> <span class="o">=</span> <span class="mi">6</span>
-        <span class="n">d_male</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span>
-            <span class="n">LibriSpeech</span><span class="p">(</span>
-                <span class="n">path</span><span class="o">=</span><span class="n">Path</span><span class="o">.</span><span class="n">home</span><span class="p">()</span> <span class="o">/</span> <span class="s2">&quot;Data&quot;</span> <span class="o">/</span> <span class="s2">&quot;Audio&quot;</span> <span class="o">/</span> <span class="s2">&quot;Speech&quot;</span> <span class="o">/</span> <span class="s2">&quot;LibriSpeech&quot;</span><span class="p">,</span>
-                <span class="n">custom_subset</span><span class="o">=</span><span class="n">LibriSpeech</span><span class="o">.</span><span class="n">CustomSubsets</span><span class="o">.</span><span class="n">male</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="p">)</span>
-        <span class="n">d_female</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span>
-            <span class="n">LibriSpeech</span><span class="p">(</span>
-                <span class="n">path</span><span class="o">=</span><span class="n">Path</span><span class="o">.</span><span class="n">home</span><span class="p">()</span> <span class="o">/</span> <span class="s2">&quot;Data&quot;</span> <span class="o">/</span> <span class="s2">&quot;Audio&quot;</span> <span class="o">/</span> <span class="s2">&quot;Speech&quot;</span> <span class="o">/</span> <span class="s2">&quot;LibriSpeech&quot;</span><span class="p">,</span>
-                <span class="n">custom_subset</span><span class="o">=</span><span class="n">LibriSpeech</span><span class="o">.</span><span class="n">CustomSubsets</span><span class="o">.</span><span class="n">female</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="p">)</span>
-        <span class="n">male_unique</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">male_unique</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">samples</span> <span class="o">//</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="n">s</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">d_male</span><span class="p">)</span>
-            <span class="n">speaker_id</span> <span class="o">=</span> <span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">speaker_id</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">male_unique</span><span class="p">:</span>
-                <span class="n">male_unique</span><span class="p">[</span><span class="n">speaker_id</span><span class="p">]</span> <span class="o">=</span> <span class="n">s</span>
-
-        <span class="n">female_unique</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">female_unique</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">samples</span> <span class="o">//</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="n">s</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">d_female</span><span class="p">)</span>
-            <span class="n">speaker_id</span> <span class="o">=</span> <span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">speaker_id</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">female_unique</span><span class="p">:</span>
-                <span class="n">female_unique</span><span class="p">[</span><span class="n">speaker_id</span><span class="p">]</span> <span class="o">=</span> <span class="n">s</span>
-
-        <span class="n">unique</span> <span class="o">=</span> <span class="p">(</span><span class="o">*</span><span class="n">male_unique</span><span class="o">.</span><span class="n">values</span><span class="p">(),</span> <span class="o">*</span><span class="n">female_unique</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
-
-        <span class="n">files</span><span class="p">,</span> <span class="n">sr</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="p">[(</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">samples</span><span class="p">),</span> <span class="n">unique</span><span class="p">)])</span>
-        <span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">sr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">s</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">sr</span><span class="p">[</span><span class="mi">1</span><span class="p">:]])</span>
-        <span class="n">sr</span> <span class="o">=</span> <span class="n">sr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="n">noise_welch</span> <span class="o">=</span> <span class="n">generate_speech_shaped_noise</span><span class="p">(</span>
-            <span class="n">files</span><span class="p">,</span> <span class="n">sr</span><span class="p">,</span> <span class="n">long_term_avg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">export_path</span><span class="o">=</span><span class="n">Path</span><span class="p">(</span><span class="s2">&quot;exclude&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;ssn_welch.wav&quot;</span>
-        <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">noise_fft</span> <span class="o">=</span> <span class="n">generate_speech_shaped_noise</span><span class="p">(</span>
-            <span class="n">files</span><span class="p">,</span> <span class="n">sr</span><span class="p">,</span> <span class="n">long_term_avg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">export_path</span><span class="o">=</span><span class="n">Path</span><span class="p">(</span><span class="s2">&quot;exclude&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;ssn_fft.wav&quot;</span>
-        <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">files</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">files</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">torchaudio</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="s2">&quot;exclude&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="s2">&quot;ssn_signal.wav&quot;</span><span class="p">),</span> <span class="n">to_tensor</span><span class="p">(</span><span class="n">files</span><span class="p">),</span> <span class="n">sr</span><span class="p">)</span>
-
-        <span class="n">ltass_plot</span><span class="p">(</span><span class="n">files</span><span class="p">,</span> <span class="n">sr</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;signal&quot;</span><span class="p">)</span>
-        <span class="n">ltass_plot</span><span class="p">(</span><span class="n">noise_welch</span><span class="p">,</span> <span class="n">sr</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;noise_welch&quot;</span><span class="p">)</span>
-        <span class="n">ltass_plot</span><span class="p">(</span><span class="n">noise_fft</span><span class="p">,</span> <span class="n">sr</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;noise_fft&quot;</span><span class="p">)</span>
-
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-
-        <span class="c1"># reald()</span>
-        <span class="c1"># synth()</span>
-        <span class="n">distinct_real</span><span class="p">()</span>
-</pre></div>
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+
+            <h1>Source code for modulation.signal_utilities.noise_generation.shaped_noise</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 09-12-2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>from</span> <span class='nn'>pathlib</span> <span class='kn'>import</span> <span class='n'>Path</span>
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Iterable</span>
+
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>import</span> <span class='nn'>torchaudio</span>
+<span class='kn'>from</span> <span class='nn'>draugr.numpy_utilities</span> <span class='kn'>import</span> <span class='n'>zero_pad_to_power_2</span>
+<span class='kn'>from</span> <span class='nn'>draugr.random_utilities.seeding</span> <span class='kn'>import</span> <span class='n'>numpy_seed</span>
+<span class='kn'>from</span> <span class='nn'>draugr.torch_utilities</span> <span class='kn'>import</span> <span class='n'>to_tensor</span>
+<span class='kn'>from</span> <span class='nn'>matplotlib</span> <span class='kn'>import</span> <span class='n'>pyplot</span>
+<span class='kn'>from</span> <span class='nn'>numpy</span> <span class='kn'>import</span> <span class='n'>fft</span>
+<span class='kn'>from</span> <span class='nn'>scipy.signal</span> <span class='kn'>import</span> <span class='n'>butter</span><span class='p'>,</span> <span class='n'>filtfilt</span><span class='p'>,</span> <span class='n'>welch</span>
+<span class='kn'>from</span> <span class='nn'>warg</span> <span class='kn'>import</span> <span class='n'>next_pow_2</span>
+
+
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[</span><span class='s2'>&quot;generate_speech_shaped_noise&quot;</span><span class='p'>]</span>
+
+<span class='kn'>from</span> <span class='nn'>modulation.signal_utilities.audio_utilities.filtering</span> <span class='kn'>import</span> <span class='n'>hilbert_envelope</span>
+
+
+<span class='k'>def</span> <span class='nf'>spectrum_like_noise</span><span class='p'>(</span>
+    <span class='n'>signal</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span>
+    <span class='o'>*</span><span class='p'>,</span>
+    <span class='n'>sampling_rate</span><span class='o'>=</span><span class='mi'>40000</span><span class='p'>,</span>
+    <span class='n'>keep_signal_amp_envelope</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>,</span>
+    <span class='n'>low_pass_cutoff</span><span class='o'>=</span><span class='mi'>50</span><span class='p'>,</span>  <span class='c1'># Hz</span>
+    <span class='n'>low_pass_order</span><span class='o'>=</span><span class='mi'>6</span><span class='p'>,</span>
+    <span class='n'>seed</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='mi'>42</span><span class='p'>,</span>
+    <span class='n'>window_length_sec</span><span class='p'>:</span> <span class='nb'>float</span> <span class='o'>=</span> <span class='mi'>20</span> <span class='o'>/</span> <span class='mi'>1000</span><span class='p'>,</span>  <span class='c1'># 20 ms</span>
+    <span class='n'>p_overlap</span><span class='p'>:</span> <span class='nb'>float</span> <span class='o'>=</span> <span class='mf'>0.5</span><span class='p'>,</span>
+    <span class='n'>long_term_avg</span><span class='p'>:</span> <span class='nb'>bool</span> <span class='o'>=</span> <span class='kc'>True</span><span class='p'>,</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;Create a noise with same spectrum as the input signal.</span>
+<span class='sd'>    randomises phase</span>
+
+<span class='sd'>    Parameters</span>
+<span class='sd'>    ----------</span>
+<span class='sd'>    signal : array_like</span>
+<span class='sd'>        Input signal.</span>
+<span class='sd'>    sampling_rate : int</span>
+<span class='sd'>         Sampling frequency of the input signal. (Default value = 40000)</span>
+<span class='sd'>    keep_signal_amp_envelope : bool</span>
+<span class='sd'>         Apply the envelope of the original signal to the noise. (Default</span>
+<span class='sd'>         value = False)</span>
+<span class='sd'>    low_pass_cutoff : float</span>
+<span class='sd'>    low_pass_order : int</span>
+<span class='sd'>    seed : int</span>
+<span class='sd'>    long_term_avg : bool</span>
+<span class='sd'>    window_length_sec: int</span>
+<span class='sd'>    p_overlap: float</span>
+
+
+<span class='sd'>    Returns</span>
+<span class='sd'>    -------</span>
+<span class='sd'>    ndarray</span>
+<span class='sd'>        Noise signal.</span>
+
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>assert</span> <span class='n'>window_length_sec</span> <span class='o'>&gt;</span> <span class='mi'>0</span>
+    <span class='k'>assert</span> <span class='mi'>0</span> <span class='o'>&lt;=</span> <span class='n'>p_overlap</span> <span class='o'>&lt;=</span> <span class='mi'>1</span>
+    <span class='n'>signal</span> <span class='o'>=</span> <span class='n'>zero_pad_to_power_2</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>)</span>  <span class='c1'># Ensure welch works with any window size</span>
+    <span class='n'>signal_length</span> <span class='o'>=</span> <span class='n'>signal</span><span class='o'>.</span><span class='n'>shape</span><span class='p'>[</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>]</span>
+    <span class='n'>window_sum_squares</span> <span class='o'>=</span> <span class='n'>signal_length</span>  <span class='c1'># scaling factor defined as sum of squared samples of window function</span>
+    <span class='n'>sc</span> <span class='o'>=</span> <span class='mi'>2</span> <span class='o'>/</span> <span class='p'>(</span>
+        <span class='n'>sampling_rate</span> <span class='o'>*</span> <span class='n'>window_sum_squares</span>
+    <span class='p'>)</span>  <span class='c1'># Scaling coefficient 2 takes into account removal of energy at negative frequencies (we drop this side of PSD)</span>
+
+    <span class='k'>if</span> <span class='ow'>not</span> <span class='n'>long_term_avg</span><span class='p'>:</span>
+        <span class='n'>n_fft</span> <span class='o'>=</span> <span class='n'>next_pow_2</span><span class='p'>(</span><span class='n'>signal_length</span><span class='p'>)</span>
+        <span class='n'>spec</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>abs</span><span class='p'>(</span><span class='n'>fft</span><span class='o'>.</span><span class='n'>rfft</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>,</span> <span class='n'>n_fft</span><span class='p'>))</span>
+        <span class='n'>psd</span> <span class='o'>=</span> <span class='p'>(</span><span class='n'>spec</span><span class='o'>**</span><span class='mi'>2</span><span class='p'>)</span> <span class='o'>*</span> <span class='n'>sc</span>
+    <span class='k'>else</span><span class='p'>:</span>
+        <span class='n'>n_per_seg</span> <span class='o'>=</span> <span class='n'>next_pow_2</span><span class='p'>(</span>
+            <span class='nb'>int</span><span class='p'>(</span><span class='n'>sampling_rate</span> <span class='o'>*</span> <span class='n'>window_length_sec</span><span class='p'>)</span>
+        <span class='p'>)</span>  <span class='c1'># next_pow_2 per seg == n_fft</span>
+        <span class='n'>n_overlap</span> <span class='o'>=</span> <span class='nb'>int</span><span class='p'>(</span><span class='n'>n_per_seg</span> <span class='o'>*</span> <span class='n'>p_overlap</span><span class='p'>)</span>
+        <span class='n'>f</span><span class='p'>,</span> <span class='n'>psd</span> <span class='o'>=</span> <span class='n'>welch</span><span class='p'>(</span>
+            <span class='n'>signal</span><span class='p'>,</span>
+            <span class='n'>sampling_rate</span><span class='p'>,</span>
+            <span class='n'>nperseg</span><span class='o'>=</span><span class='n'>n_per_seg</span><span class='p'>,</span>
+            <span class='n'>noverlap</span><span class='o'>=</span><span class='n'>n_overlap</span><span class='p'>,</span>
+            <span class='n'>scaling</span><span class='o'>=</span><span class='s2'>&quot;density&quot;</span><span class='p'>,</span>
+            <span class='n'>return_onesided</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>,</span>
+            <span class='n'>detrend</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>,</span>
+            <span class='c1'># window=&#39;boxcar&#39;,</span>
+            <span class='n'>window</span><span class='o'>=</span><span class='s2'>&quot;hanning&quot;</span><span class='p'>,</span>
+        <span class='p'>)</span>
+        <span class='n'>n_fft</span> <span class='o'>=</span> <span class='n'>n_per_seg</span>
+        <span class='n'>psd</span> <span class='o'>/=</span> <span class='n'>signal_length</span> <span class='o'>/</span> <span class='n'>n_per_seg</span>  <span class='c1'># normalise?</span>
+    <span class='n'>spec</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sqrt</span><span class='p'>((</span><span class='n'>psd</span> <span class='o'>/</span> <span class='n'>sc</span><span class='p'>))</span>
+
+    <span class='n'>noise</span> <span class='o'>=</span> <span class='p'>[]</span>
+    <span class='n'>runs</span> <span class='o'>=</span> <span class='n'>signal_length</span> <span class='o'>//</span> <span class='n'>n_fft</span>
+    <span class='k'>for</span> <span class='n'>i</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='n'>runs</span> <span class='o'>+</span> <span class='mi'>1</span><span class='p'>):</span>
+        <span class='n'>numpy_seed</span><span class='p'>(</span><span class='n'>seed</span> <span class='o'>+</span> <span class='n'>i</span><span class='p'>)</span>
+        <span class='n'>noise</span><span class='o'>.</span><span class='n'>extend</span><span class='p'>(</span>
+            <span class='n'>numpy</span><span class='o'>.</span><span class='n'>real</span><span class='p'>(</span>
+                <span class='n'>fft</span><span class='o'>.</span><span class='n'>irfft</span><span class='p'>(</span>
+                    <span class='n'>spec</span>
+                    <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>exp</span><span class='p'>(</span>
+                        <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>pi</span> <span class='o'>*</span> <span class='mi'>1</span><span class='n'>j</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>random</span><span class='o'>.</span><span class='n'>random</span><span class='p'>(</span><span class='n'>spec</span><span class='o'>.</span><span class='n'>shape</span><span class='p'>[</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>])</span>
+                    <span class='p'>),</span>  <span class='c1'># Randomise phase. 0-&gt;360, 2 pi rads</span>
+                    <span class='n'>n_fft</span><span class='p'>,</span>
+                <span class='p'>)</span>
+            <span class='p'>)</span>
+        <span class='p'>)</span>  <span class='c1'># Give each spectral component a random phase, PHI(f(k)) = random number,</span>
+        <span class='c1'># uniformly distributed between 0 and 360 degrees (or equivalently, between 0 and 2Pi radians);</span>
+
+    <span class='n'>noise</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>(</span><span class='n'>noise</span><span class='p'>)[:</span><span class='n'>signal_length</span><span class='p'>]</span>
+
+    <span class='k'>if</span> <span class='n'>keep_signal_amp_envelope</span><span class='p'>:</span>
+        <span class='p'>[</span><span class='n'>bb</span><span class='p'>,</span> <span class='n'>aa</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>butter</span><span class='p'>(</span>
+            <span class='n'>low_pass_order</span><span class='p'>,</span> <span class='n'>low_pass_cutoff</span> <span class='o'>/</span> <span class='p'>(</span><span class='n'>sampling_rate</span> <span class='o'>/</span> <span class='mi'>2</span><span class='p'>)</span>
+        <span class='p'>)</span>  <span class='c1'># Cutoff Hz, LP filter</span>
+        <span class='n'>noise</span> <span class='o'>*=</span> <span class='n'>filtfilt</span><span class='p'>(</span>
+            <span class='n'>bb</span><span class='p'>,</span>  <span class='c1'># numerator</span>
+            <span class='n'>aa</span><span class='p'>,</span>  <span class='c1'># denominator</span>
+            <span class='n'>hilbert_envelope</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>),</span>  <span class='c1'># envelope of speech signal in time domain</span>
+        <span class='p'>)</span>
+
+    <span class='k'>return</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>expand_dims</span><span class='p'>(</span><span class='n'>noise</span><span class='p'>,</span> <span class='mi'>0</span><span class='p'>)</span>
+
+
+<div class='viewcode-block' id='generate_speech_shaped_noise'><a class='viewcode-back' href='../../../../generated/modulation.signal_utilities.noise_generation.shaped_noise.generate_speech_shaped_noise.html#modulation.signal_utilities.noise_generation.shaped_noise.generate_speech_shaped_noise'>[docs]</a><span class='k'>def</span> <span class='nf'>generate_speech_shaped_noise</span><span class='p'>(</span>
+    <span class='n'>samples</span><span class='p'>:</span> <span class='n'>Iterable</span><span class='p'>[</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>],</span>
+    <span class='n'>sampling_rate</span><span class='p'>,</span>
+    <span class='o'>*</span><span class='p'>,</span>
+    <span class='n'>export_path</span><span class='p'>:</span> <span class='n'>Path</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>,</span>
+    <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>,</span>
+    <span class='n'>window_length_sec</span><span class='o'>=</span><span class='mi'>20</span> <span class='o'>/</span> <span class='mi'>1000</span><span class='p'>,</span>
+    <span class='n'>p_overlap</span><span class='o'>=</span><span class='mf'>0.5</span><span class='p'>,</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Iterable</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    White (flat-spectrum) noise has more energy at high frequencies, and therefore produces more masking of the higher formants and frication noises than speech-shaped noise, speech shaped noise mask evenly throughout the speech signal spectrum</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+    <span class='n'>samples</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>concatenate</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>,</span> <span class='o'>-</span><span class='mi'>1</span><span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+    <span class='n'>noise</span> <span class='o'>=</span> <span class='n'>spectrum_like_noise</span><span class='p'>(</span>
+        <span class='n'>samples</span><span class='p'>,</span>
+        <span class='n'>sampling_rate</span><span class='o'>=</span><span class='n'>sampling_rate</span><span class='p'>,</span>
+        <span class='n'>long_term_avg</span><span class='o'>=</span><span class='n'>long_term_avg</span><span class='p'>,</span>
+        <span class='n'>window_length_sec</span><span class='o'>=</span><span class='n'>window_length_sec</span><span class='p'>,</span>
+        <span class='n'>p_overlap</span><span class='o'>=</span><span class='n'>p_overlap</span><span class='p'>,</span>
+    <span class='p'>)</span>
+    <span class='k'>if</span> <span class='n'>export_path</span><span class='p'>:</span>
+        <span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>save</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='n'>export_path</span><span class='p'>),</span> <span class='n'>to_tensor</span><span class='p'>(</span><span class='n'>noise</span><span class='p'>),</span> <span class='n'>sampling_rate</span><span class='p'>)</span>
+    <span class='k'>return</span> <span class='n'>noise</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+
+    <span class='k'>def</span> <span class='nf'>reald</span><span class='p'>():</span>
+        <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+        <span class='kn'>from</span> <span class='nn'>draugr.visualisation</span> <span class='kn'>import</span> <span class='n'>ltass_plot</span>
+        <span class='kn'>from</span> <span class='nn'>modulation.data.audio.speech.recognition.libri_speech</span> <span class='kn'>import</span> <span class='n'>LibriSpeech</span>
+
+        <span class='n'>libri_speech</span> <span class='o'>=</span> <span class='n'>LibriSpeech</span><span class='p'>(</span>
+            <span class='n'>path</span><span class='o'>=</span><span class='n'>Path</span><span class='o'>.</span><span class='n'>home</span><span class='p'>()</span> <span class='o'>/</span> <span class='s2'>&quot;Data&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Audio&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Speech&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;LibriSpeech&quot;</span>
+        <span class='p'>)</span>
+        <span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span> <span class='o'>=</span> <span class='nb'>zip</span><span class='p'>(</span>
+            <span class='o'>*</span><span class='p'>[(</span><span class='n'>v</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span><span class='o'>.</span><span class='n'>numpy</span><span class='p'>(),</span> <span class='n'>v</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>])</span> <span class='k'>for</span> <span class='n'>_</span><span class='p'>,</span> <span class='n'>v</span> <span class='ow'>in</span> <span class='nb'>zip</span><span class='p'>(</span><span class='nb'>range</span><span class='p'>(</span><span class='mi'>20</span><span class='p'>),</span> <span class='n'>libri_speech</span><span class='p'>)]</span>
+        <span class='p'>)</span>
+        <span class='k'>assert</span> <span class='nb'>all</span><span class='p'>([</span><span class='n'>sr</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span> <span class='o'>==</span> <span class='n'>s</span> <span class='k'>for</span> <span class='n'>s</span> <span class='ow'>in</span> <span class='n'>sr</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:]])</span>
+        <span class='n'>sr</span> <span class='o'>=</span> <span class='n'>sr</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span>
+
+        <span class='c1'># from modulation.noise_generation import generate_babble_noise</span>
+        <span class='c1'># files = generate_babble_noise(samples, sampling_rate)</span>
+        <span class='n'>samples_len</span> <span class='o'>=</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>concatenate</span><span class='p'>(</span><span class='n'>files</span><span class='p'>,</span> <span class='o'>-</span><span class='mi'>1</span><span class='p'>)[</span><span class='mi'>0</span><span class='p'>])</span>
+
+        <span class='n'>noise_welch</span> <span class='o'>=</span> <span class='n'>generate_speech_shaped_noise</span><span class='p'>(</span>
+            <span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>,</span> <span class='n'>export_path</span><span class='o'>=</span><span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='s2'>&quot;ssn_welch.wav&quot;</span>
+        <span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+        <span class='n'>noise_fft</span> <span class='o'>=</span> <span class='n'>generate_speech_shaped_noise</span><span class='p'>(</span>
+            <span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>,</span> <span class='n'>export_path</span><span class='o'>=</span><span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='s2'>&quot;ssn_fft.wav&quot;</span>
+        <span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+        <span class='c1'># noise_welch_full = generate_speech_shaped_noise(files, sr, long_term_avg=True, window_length_sec= samples_len/sr,p_overlap=0)[0]</span>
+        <span class='n'>files</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>concatenate</span><span class='p'>(</span><span class='n'>files</span><span class='p'>,</span> <span class='o'>-</span><span class='mi'>1</span><span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+        <span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>save</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='s2'>&quot;ssn_signal.wav&quot;</span><span class='p'>),</span> <span class='n'>to_tensor</span><span class='p'>(</span><span class='n'>files</span><span class='p'>),</span> <span class='n'>sr</span><span class='p'>)</span>
+
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;signal&quot;</span><span class='p'>)</span>
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>noise_welch</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;noise_welch&quot;</span><span class='p'>)</span>
+        <span class='c1'># ltass_plot(noise_welch_full, sr, label=&#39;noise_welch_full&#39;)</span>
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>noise_fft</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;noise_fft&quot;</span><span class='p'>)</span>
+
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>legend</span><span class='p'>()</span>
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>show</span><span class='p'>()</span>
+
+    <span class='k'>def</span> <span class='nf'>synth</span><span class='p'>():</span>
+        <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+        <span class='kn'>from</span> <span class='nn'>draugr.visualisation</span> <span class='kn'>import</span> <span class='n'>ltass_plot</span>
+        <span class='kn'>from</span> <span class='nn'>modulation.data.audio.speech.recognition.libri_speech</span> <span class='kn'>import</span> <span class='n'>LibriSpeech</span>
+
+        <span class='n'>signal_length_sec</span> <span class='o'>=</span> <span class='mi'>11</span>
+        <span class='n'>t</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>linspace</span><span class='p'>(</span>
+            <span class='mi'>0</span><span class='p'>,</span> <span class='n'>signal_length_sec</span><span class='p'>,</span> <span class='nb'>int</span><span class='p'>(</span><span class='mi'>1000</span> <span class='o'>*</span> <span class='n'>signal_length_sec</span><span class='p'>),</span> <span class='n'>endpoint</span><span class='o'>=</span><span class='kc'>False</span>
+        <span class='p'>)</span>
+        <span class='n'>signal</span> <span class='o'>=</span> <span class='p'>(</span>
+            <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sin</span><span class='p'>(</span><span class='mi'>50</span> <span class='o'>*</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>pi</span> <span class='o'>*</span> <span class='n'>t</span><span class='p'>)</span>
+            <span class='o'>+</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sin</span><span class='p'>(</span><span class='mi'>200</span> <span class='o'>*</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>pi</span> <span class='o'>*</span> <span class='n'>t</span><span class='p'>)</span>
+            <span class='o'>+</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>sin</span><span class='p'>(</span><span class='mi'>400</span> <span class='o'>*</span> <span class='mi'>2</span> <span class='o'>*</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>pi</span> <span class='o'>*</span> <span class='n'>t</span><span class='p'>)</span>
+        <span class='p'>)</span>
+        <span class='n'>sr</span> <span class='o'>=</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>t</span><span class='p'>)</span> <span class='o'>/</span> <span class='n'>signal_length_sec</span>
+
+        <span class='n'>noise_welch</span> <span class='o'>=</span> <span class='n'>spectrum_like_noise</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>,</span> <span class='n'>sampling_rate</span><span class='o'>=</span><span class='n'>sr</span><span class='p'>,</span> <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>)[</span>
+            <span class='mi'>0</span>
+        <span class='p'>]</span>
+        <span class='n'>noise_welch_full</span> <span class='o'>=</span> <span class='n'>spectrum_like_noise</span><span class='p'>(</span>
+            <span class='n'>signal</span><span class='p'>,</span>
+            <span class='n'>sampling_rate</span><span class='o'>=</span><span class='n'>sr</span><span class='p'>,</span>
+            <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>,</span>
+            <span class='n'>window_length_sec</span><span class='o'>=</span><span class='n'>signal_length_sec</span><span class='p'>,</span>
+            <span class='n'>p_overlap</span><span class='o'>=</span><span class='mi'>0</span><span class='p'>,</span>
+        <span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+        <span class='n'>noise_fft</span> <span class='o'>=</span> <span class='n'>spectrum_like_noise</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>,</span> <span class='n'>sampling_rate</span><span class='o'>=</span><span class='n'>sr</span><span class='p'>,</span> <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>)[</span>
+            <span class='mi'>0</span>
+        <span class='p'>]</span>
+
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;signal&quot;</span><span class='p'>)</span>
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>noise_welch</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;noise_welch&quot;</span><span class='p'>)</span>
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>noise_welch_full</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;noise_welch_full&quot;</span><span class='p'>)</span>
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>noise_fft</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;noise_fft&quot;</span><span class='p'>)</span>
+
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>legend</span><span class='p'>()</span>
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>show</span><span class='p'>()</span>
+
+    <span class='k'>def</span> <span class='nf'>distinct_real</span><span class='p'>():</span>
+        <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+        <span class='kn'>from</span> <span class='nn'>draugr.visualisation</span> <span class='kn'>import</span> <span class='n'>ltass_plot</span>
+        <span class='kn'>from</span> <span class='nn'>modulation.data.audio.speech.recognition.libri_speech</span> <span class='kn'>import</span> <span class='n'>LibriSpeech</span>
+
+        <span class='n'>samples</span> <span class='o'>=</span> <span class='mi'>6</span>
+        <span class='n'>d_male</span> <span class='o'>=</span> <span class='nb'>iter</span><span class='p'>(</span>
+            <span class='n'>LibriSpeech</span><span class='p'>(</span>
+                <span class='n'>path</span><span class='o'>=</span><span class='n'>Path</span><span class='o'>.</span><span class='n'>home</span><span class='p'>()</span> <span class='o'>/</span> <span class='s2'>&quot;Data&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Audio&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Speech&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;LibriSpeech&quot;</span><span class='p'>,</span>
+                <span class='n'>custom_subset</span><span class='o'>=</span><span class='n'>LibriSpeech</span><span class='o'>.</span><span class='n'>CustomSubsets</span><span class='o'>.</span><span class='n'>male</span><span class='p'>,</span>
+            <span class='p'>)</span>
+        <span class='p'>)</span>
+        <span class='n'>d_female</span> <span class='o'>=</span> <span class='nb'>iter</span><span class='p'>(</span>
+            <span class='n'>LibriSpeech</span><span class='p'>(</span>
+                <span class='n'>path</span><span class='o'>=</span><span class='n'>Path</span><span class='o'>.</span><span class='n'>home</span><span class='p'>()</span> <span class='o'>/</span> <span class='s2'>&quot;Data&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Audio&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;Speech&quot;</span> <span class='o'>/</span> <span class='s2'>&quot;LibriSpeech&quot;</span><span class='p'>,</span>
+                <span class='n'>custom_subset</span><span class='o'>=</span><span class='n'>LibriSpeech</span><span class='o'>.</span><span class='n'>CustomSubsets</span><span class='o'>.</span><span class='n'>female</span><span class='p'>,</span>
+            <span class='p'>)</span>
+        <span class='p'>)</span>
+        <span class='n'>male_unique</span> <span class='o'>=</span> <span class='p'>{}</span>
+        <span class='k'>while</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>male_unique</span><span class='p'>)</span> <span class='o'>&lt;</span> <span class='n'>samples</span> <span class='o'>//</span> <span class='mi'>2</span><span class='p'>:</span>
+            <span class='n'>s</span> <span class='o'>=</span> <span class='nb'>next</span><span class='p'>(</span><span class='n'>d_male</span><span class='p'>)</span>
+            <span class='n'>speaker_id</span> <span class='o'>=</span> <span class='n'>s</span><span class='p'>[</span><span class='o'>-</span><span class='mi'>3</span><span class='p'>]</span>
+            <span class='k'>if</span> <span class='n'>speaker_id</span> <span class='ow'>not</span> <span class='ow'>in</span> <span class='n'>male_unique</span><span class='p'>:</span>
+                <span class='n'>male_unique</span><span class='p'>[</span><span class='n'>speaker_id</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>s</span>
+
+        <span class='n'>female_unique</span> <span class='o'>=</span> <span class='p'>{}</span>
+        <span class='k'>while</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>female_unique</span><span class='p'>)</span> <span class='o'>&lt;</span> <span class='n'>samples</span> <span class='o'>//</span> <span class='mi'>2</span><span class='p'>:</span>
+            <span class='n'>s</span> <span class='o'>=</span> <span class='nb'>next</span><span class='p'>(</span><span class='n'>d_female</span><span class='p'>)</span>
+            <span class='n'>speaker_id</span> <span class='o'>=</span> <span class='n'>s</span><span class='p'>[</span><span class='o'>-</span><span class='mi'>3</span><span class='p'>]</span>
+            <span class='k'>if</span> <span class='n'>speaker_id</span> <span class='ow'>not</span> <span class='ow'>in</span> <span class='n'>female_unique</span><span class='p'>:</span>
+                <span class='n'>female_unique</span><span class='p'>[</span><span class='n'>speaker_id</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>s</span>
+
+        <span class='n'>unique</span> <span class='o'>=</span> <span class='p'>(</span><span class='o'>*</span><span class='n'>male_unique</span><span class='o'>.</span><span class='n'>values</span><span class='p'>(),</span> <span class='o'>*</span><span class='n'>female_unique</span><span class='o'>.</span><span class='n'>values</span><span class='p'>())</span>
+
+        <span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span> <span class='o'>=</span> <span class='nb'>zip</span><span class='p'>(</span><span class='o'>*</span><span class='p'>[(</span><span class='n'>v</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span><span class='o'>.</span><span class='n'>numpy</span><span class='p'>(),</span> <span class='n'>v</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>])</span> <span class='k'>for</span> <span class='n'>_</span><span class='p'>,</span> <span class='n'>v</span> <span class='ow'>in</span> <span class='nb'>zip</span><span class='p'>(</span><span class='nb'>range</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>),</span> <span class='n'>unique</span><span class='p'>)])</span>
+        <span class='k'>assert</span> <span class='nb'>all</span><span class='p'>([</span><span class='n'>sr</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span> <span class='o'>==</span> <span class='n'>s</span> <span class='k'>for</span> <span class='n'>s</span> <span class='ow'>in</span> <span class='n'>sr</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:]])</span>
+        <span class='n'>sr</span> <span class='o'>=</span> <span class='n'>sr</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span>
+
+        <span class='n'>noise_welch</span> <span class='o'>=</span> <span class='n'>generate_speech_shaped_noise</span><span class='p'>(</span>
+            <span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>,</span> <span class='n'>export_path</span><span class='o'>=</span><span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='s2'>&quot;ssn_welch.wav&quot;</span>
+        <span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+        <span class='n'>noise_fft</span> <span class='o'>=</span> <span class='n'>generate_speech_shaped_noise</span><span class='p'>(</span>
+            <span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>long_term_avg</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>,</span> <span class='n'>export_path</span><span class='o'>=</span><span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='s2'>&quot;ssn_fft.wav&quot;</span>
+        <span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+        <span class='n'>files</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>concatenate</span><span class='p'>(</span><span class='n'>files</span><span class='p'>,</span> <span class='o'>-</span><span class='mi'>1</span><span class='p'>)[</span><span class='mi'>0</span><span class='p'>]</span>
+        <span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>save</span><span class='p'>(</span><span class='nb'>str</span><span class='p'>(</span><span class='n'>Path</span><span class='p'>(</span><span class='s2'>&quot;exclude&quot;</span><span class='p'>)</span> <span class='o'>/</span> <span class='s2'>&quot;ssn_signal.wav&quot;</span><span class='p'>),</span> <span class='n'>to_tensor</span><span class='p'>(</span><span class='n'>files</span><span class='p'>),</span> <span class='n'>sr</span><span class='p'>)</span>
+
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>files</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;signal&quot;</span><span class='p'>)</span>
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>noise_welch</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;noise_welch&quot;</span><span class='p'>)</span>
+        <span class='n'>ltass_plot</span><span class='p'>(</span><span class='n'>noise_fft</span><span class='p'>,</span> <span class='n'>sr</span><span class='p'>,</span> <span class='n'>label</span><span class='o'>=</span><span class='s2'>&quot;noise_fft&quot;</span><span class='p'>)</span>
+
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>legend</span><span class='p'>()</span>
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>show</span><span class='p'>()</span>
+
+        <span class='c1'># reald()</span>
+        <span class='c1'># synth()</span>
+        <span class='n'>distinct_real</span><span class='p'>()</span>
+</pre>
+            </div>
 
           </div>
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+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
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diff --git a/docs/github/html/_modules/modulation/signal_utilities/segmentation.html b/docs/github/html/_modules/modulation/signal_utilities/segmentation.html
index 3b80969..57150b4 100644
--- a/docs/github/html/_modules/modulation/signal_utilities/segmentation.html
+++ b/docs/github/html/_modules/modulation/signal_utilities/segmentation.html
@@ -1,145 +1,139 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
     <title>modulation.signal_utilities.segmentation &#8212; modulation 0.0.1 documentation</title>
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+    <link href='../../../search.html' rel='search' title='Search'/>
 
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+    <link href='../../../_static/custom.css' rel='stylesheet' type='text/css'/>
 
-    <div class="document">
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-        <div class="bodywrapper">
-          
 
-          <div class="body" role="main">
-            
-  <h1>Source code for modulation.signal_utilities.segmentation</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
+    <meta content='width=device-width, initial-scale=0.9, maximum-scale=0.9' name='viewport'/>
 
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;heider&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
+  </head>
+  <body>
 
-<span class="s2">           Created on 8/1/22</span>
-<span class="s2">           &quot;&quot;&quot;</span>
 
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;runs_of_non_zero&quot;</span><span class="p">]</span>
+    <div class='document'>
+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
 
-<span class="kn">import</span> <span class="nn">itertools</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Sequence</span>
 
-<span class="kn">from</span> <span class="nn">warg</span> <span class="kn">import</span> <span class="n">Number</span>
+          <div class='body' role='main'>
 
+            <h1>Source code for modulation.signal_utilities.segmentation</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
 
-<span class="k">def</span> <span class="nf">non_zero</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">Number</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-    <span class="k">return</span> <span class="n">x</span> <span class="o">!=</span> <span class="mi">0</span>
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;heider&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
 
+<span class='s2'>           Created on 8/1/22</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
 
-<div class="viewcode-block" id="runs_of_non_zero"><a class="viewcode-back" href="../../../generated/modulation.signal_utilities.segmentation.runs_of_non_zero.html#modulation.signal_utilities.segmentation.runs_of_non_zero">[docs]</a><span class="k">def</span> <span class="nf">runs_of_non_zero</span><span class="p">(</span><span class="n">bits</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Sequence</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Finds runs of non-zero bits in a sequence.</span>
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[</span><span class='s2'>&quot;runs_of_non_zero&quot;</span><span class='p'>]</span>
 
-<span class="sd">    :param bits:</span>
-<span class="sd">    :type bits:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
+<span class='kn'>import</span> <span class='nn'>itertools</span>
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Sequence</span>
 
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">for</span> <span class="n">bit</span><span class="p">,</span> <span class="n">group</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">bits</span><span class="p">,</span> <span class="n">non_zero</span><span class="p">):</span>
-        <span class="k">yield</span> <span class="n">bit</span><span class="p">,</span> <span class="n">group</span></div>
-</pre></div>
+<span class='kn'>from</span> <span class='nn'>warg</span> <span class='kn'>import</span> <span class='n'>Number</span>
 
-          </div>
-          
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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-<h1 class="logo"><a href="../../../index.html">modulation</a></h1>
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-<h3>Navigation</h3>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../generated/modulation.html">modulation</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notes</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../getting_started.html">Getting Started</a></li>
-</ul>
-
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../../../index.html">Documentation overview</a><ul>
-  <li><a href="../../index.html">Module code</a><ul>
-  <li><a href="../../modulation.html">modulation</a><ul>
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-      <input type="submit" value="Go" />
-    </form>
-    </div>
-</div>
-<script>document.getElementById('searchbox').style.display = "block"</script>
 
+<span class='k'>def</span> <span class='nf'>non_zero</span><span class='p'>(</span><span class='n'>x</span><span class='p'>:</span> <span class='n'>Number</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='nb'>bool</span><span class='p'>:</span>
+    <span class='k'>return</span> <span class='n'>x</span> <span class='o'>!=</span> <span class='mi'>0</span>
 
 
+<div class='viewcode-block' id='runs_of_non_zero'><a class='viewcode-back' href='../../../generated/modulation.signal_utilities.segmentation.runs_of_non_zero.html#modulation.signal_utilities.segmentation.runs_of_non_zero'>[docs]</a><span class='k'>def</span> <span class='nf'>runs_of_non_zero</span><span class='p'>(</span><span class='n'>bits</span><span class='p'>:</span> <span class='n'>Sequence</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Sequence</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    Finds runs of non-zero bits in a sequence.</span>
 
+<span class='sd'>    :param bits:</span>
+<span class='sd'>    :type bits:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
 
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='k'>for</span> <span class='n'>bit</span><span class='p'>,</span> <span class='n'>group</span> <span class='ow'>in</span> <span class='n'>itertools</span><span class='o'>.</span><span class='n'>groupby</span><span class='p'>(</span><span class='n'>bits</span><span class='p'>,</span> <span class='n'>non_zero</span><span class='p'>):</span>
+        <span class='k'>yield</span> <span class='n'>bit</span><span class='p'>,</span> <span class='n'>group</span></div>
+</pre>
+            </div>
 
+          </div>
+
+        </div>
+      </div>
+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
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+          <p class='logo'><a href='../../../index.html'>
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+          </a></p>
+          <h1 class='logo'><a href='../../../index.html'>modulation</a></h1>
+
+
+          <h3>Navigation</h3>
+          <ul>
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../../../index.html'>Documentation overview</a>
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+                  <li><a href='../../index.html'>Module code</a>
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+                      <li><a href='../../modulation.html'>modulation</a>
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+              <form action='../../../search.html' class='search' method='get'>
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+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
 
 
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+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
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+
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-    
 
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diff --git a/docs/github/html/_modules/modulation/signal_utilities/splitting.html b/docs/github/html/_modules/modulation/signal_utilities/splitting.html
index d842be8..643d409 100644
--- a/docs/github/html/_modules/modulation/signal_utilities/splitting.html
+++ b/docs/github/html/_modules/modulation/signal_utilities/splitting.html
@@ -1,203 +1,197 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
     <title>modulation.signal_utilities.splitting &#8212; modulation 0.0.1 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/graphviz.css" />
-    <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
-    <script src="../../../_static/jquery.js"></script>
-    <script src="../../../_static/underscore.js"></script>
-    <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
-    <script src="../../../_static/doctools.js"></script>
-    <link rel="canonical" href="Aivclab.github.io/modulation/_modules/modulation/signal_utilities/splitting.html" />
-    <link rel="index" title="Index" href="../../../genindex.html" />
-    <link rel="search" title="Search" href="../../../search.html" />
-   
-  <link rel="stylesheet" href="../../../_static/custom.css" type="text/css" />
-  
-  
-  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
-
-  </head><body>
-  
-
-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          
-
-          <div class="body" role="main">
-            
-  <h1>Source code for modulation.signal_utilities.splitting</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 15-01-2021</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;mask_split_non_zero_concat&quot;</span><span class="p">,</span> <span class="s2">&quot;mask_split_non_zero_segments&quot;</span><span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">modulation.signal_utilities.segmentation</span> <span class="kn">import</span> <span class="n">runs_of_non_zero</span>
-
-
-<div class="viewcode-block" id="mask_split_non_zero_concat"><a class="viewcode-back" href="../../../generated/modulation.signal_utilities.splitting.mask_split_non_zero_concat.html#modulation.signal_utilities.splitting.mask_split_non_zero_concat">[docs]</a><span class="k">def</span> <span class="nf">mask_split_non_zero_concat</span><span class="p">(</span>
-    <span class="n">mask</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">Sequence</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    discards rest of the data if mask is shorter than data</span>
-<span class="sd">    a is where the mask is 1</span>
-<span class="sd">    b is where the mask is 0</span>
-
-<span class="sd">    :param mask:</span>
-<span class="sd">    :type mask:</span>
-<span class="sd">    :param data:</span>
-<span class="sd">    :type data:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">a</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">b</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">mask</span><span class="p">))):</span>
-        <span class="k">if</span> <span class="n">mask</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">b</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">n</span><span class="p">])</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">a</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">n</span><span class="p">])</span>
-
-    <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">a</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">b</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="mask_split_non_zero_segments"><a class="viewcode-back" href="../../../generated/modulation.signal_utilities.splitting.mask_split_non_zero_segments.html#modulation.signal_utilities.splitting.mask_split_non_zero_segments">[docs]</a><span class="k">def</span> <span class="nf">mask_split_non_zero_segments</span><span class="p">(</span>
-    <span class="n">mask</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">Sequence</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Sequence</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">]:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    discards rest of the data if vad is shorter than data</span>
-
-<span class="sd">    a is where the mask is 1</span>
-<span class="sd">    b is where the mask is 0</span>
-
-<span class="sd">    :param mask:</span>
-<span class="sd">    :type mask:</span>
-<span class="sd">    :param data:</span>
-<span class="sd">    :type data:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">data_masked</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">mask</span><span class="p">))):</span>
-        <span class="k">if</span> <span class="n">mask</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">data_masked</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">data_masked</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">n</span><span class="p">]</span>
-
-    <span class="n">a</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">b</span> <span class="o">=</span> <span class="p">[]</span>
-
-    <span class="k">for</span> <span class="n">bit</span><span class="p">,</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">runs_of_non_zero</span><span class="p">(</span><span class="n">data_masked</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">bit</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">a</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">r</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">b</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">r</span><span class="p">))</span>
-
-    <span class="k">return</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-    <span class="n">a</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-    <span class="n">b</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">mask_split_non_zero_concat</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
-
-    <span class="n">c</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-    <span class="n">c</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-    <span class="n">c</span><span class="p">[</span><span class="mi">8</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+    <link href='../../../_static/pygments.css' rel='stylesheet' type='text/css'/>
+    <link href='../../../_static/alabaster.css' rel='stylesheet' type='text/css'/>
+    <link href='../../../_static/graphviz.css' rel='stylesheet' type='text/css'/>
+    <script data-url_root='../../../' id='documentation_options' src='../../../_static/documentation_options.js'></script>
+    <script src='../../../_static/jquery.js'></script>
+    <script src='../../../_static/underscore.js'></script>
+    <script src='../../../_static/_sphinx_javascript_frameworks_compat.js'></script>
+    <script src='../../../_static/doctools.js'></script>
+    <link href='Aivclab.github.io/modulation/_modules/modulation/signal_utilities/splitting.html' rel='canonical'/>
+    <link href='../../../genindex.html' rel='index' title='Index'/>
+    <link href='../../../search.html' rel='search' title='Search'/>
 
-    <span class="n">s</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">mask_split_non_zero_segments</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">s</span><span class="p">))</span>
-</pre></div>
+    <link href='../../../_static/custom.css' rel='stylesheet' type='text/css'/>
+
+
+    <meta content='width=device-width, initial-scale=0.9, maximum-scale=0.9' name='viewport'/>
+
+  </head>
+  <body>
+
+
+    <div class='document'>
+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
+
+
+          <div class='body' role='main'>
+
+            <h1>Source code for modulation.signal_utilities.splitting</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 15-01-2021</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>Tuple</span>
+
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[</span><span class='s2'>&quot;mask_split_non_zero_concat&quot;</span><span class='p'>,</span> <span class='s2'>&quot;mask_split_non_zero_segments&quot;</span><span class='p'>]</span>
+
+<span class='kn'>from</span> <span class='nn'>modulation.signal_utilities.segmentation</span> <span class='kn'>import</span> <span class='n'>runs_of_non_zero</span>
 
-          </div>
-          
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-            <p class="logo"><a href="../../../index.html">
-              <img class="logo" src="../../../_static/mod.svg" alt="Logo"/>
-            </a></p>
-<h1 class="logo"><a href="../../../index.html">modulation</a></h1>
-
-
-
-
-
-
-
-
-<h3>Navigation</h3>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../generated/modulation.html">modulation</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notes</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../getting_started.html">Getting Started</a></li>
-</ul>
-
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../../../index.html">Documentation overview</a><ul>
-  <li><a href="../../index.html">Module code</a><ul>
-  <li><a href="../../modulation.html">modulation</a><ul>
-  </ul></li>
-  </ul></li>
-  </ul></li>
-</ul>
-</div>
-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../../../search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
-      <input type="submit" value="Go" />
-    </form>
-    </div>
-</div>
-<script>document.getElementById('searchbox').style.display = "block"</script>
 
+<div class='viewcode-block' id='mask_split_non_zero_concat'><a class='viewcode-back' href='../../../generated/modulation.signal_utilities.splitting.mask_split_non_zero_concat.html#modulation.signal_utilities.splitting.mask_split_non_zero_concat'>[docs]</a><span class='k'>def</span> <span class='nf'>mask_split_non_zero_concat</span><span class='p'>(</span>
+    <span class='n'>mask</span><span class='p'>:</span> <span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>data</span><span class='p'>:</span> <span class='n'>Sequence</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Tuple</span><span class='p'>[</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>]:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    discards rest of the data if mask is shorter than data</span>
+<span class='sd'>    a is where the mask is 1</span>
+<span class='sd'>    b is where the mask is 0</span>
 
+<span class='sd'>    :param mask:</span>
+<span class='sd'>    :type mask:</span>
+<span class='sd'>    :param data:</span>
+<span class='sd'>    :type data:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>a</span> <span class='o'>=</span> <span class='p'>[]</span>
+    <span class='n'>b</span> <span class='o'>=</span> <span class='p'>[]</span>
+    <span class='k'>for</span> <span class='n'>n</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='nb'>min</span><span class='p'>(</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>data</span><span class='p'>),</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>mask</span><span class='p'>))):</span>
+        <span class='k'>if</span> <span class='n'>mask</span><span class='p'>[</span><span class='n'>n</span><span class='p'>]</span> <span class='o'>==</span> <span class='mi'>0</span><span class='p'>:</span>
+            <span class='n'>b</span><span class='o'>.</span><span class='n'>append</span><span class='p'>(</span><span class='n'>data</span><span class='p'>[</span><span class='n'>n</span><span class='p'>])</span>
+        <span class='k'>else</span><span class='p'>:</span>
+            <span class='n'>a</span><span class='o'>.</span><span class='n'>append</span><span class='p'>(</span><span class='n'>data</span><span class='p'>[</span><span class='n'>n</span><span class='p'>])</span>
 
+    <span class='k'>return</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>asarray</span><span class='p'>(</span><span class='n'>a</span><span class='p'>),</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>asarray</span><span class='p'>(</span><span class='n'>b</span><span class='p'>)</span></div>
 
 
+<div class='viewcode-block' id='mask_split_non_zero_segments'><a class='viewcode-back' href='../../../generated/modulation.signal_utilities.splitting.mask_split_non_zero_segments.html#modulation.signal_utilities.splitting.mask_split_non_zero_segments'>[docs]</a><span class='k'>def</span> <span class='nf'>mask_split_non_zero_segments</span><span class='p'>(</span>
+    <span class='n'>mask</span><span class='p'>:</span> <span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>data</span><span class='p'>:</span> <span class='n'>Sequence</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Tuple</span><span class='p'>[</span><span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>Sequence</span><span class='p'>]:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    discards rest of the data if vad is shorter than data</span>
 
+<span class='sd'>    a is where the mask is 1</span>
+<span class='sd'>    b is where the mask is 0</span>
 
+<span class='sd'>    :param mask:</span>
+<span class='sd'>    :type mask:</span>
+<span class='sd'>    :param data:</span>
+<span class='sd'>    :type data:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>data_masked</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>zeros_like</span><span class='p'>(</span><span class='n'>data</span><span class='p'>)</span>
+
+    <span class='k'>for</span> <span class='n'>n</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='nb'>min</span><span class='p'>(</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>data</span><span class='p'>),</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>mask</span><span class='p'>))):</span>
+        <span class='k'>if</span> <span class='n'>mask</span><span class='p'>[</span><span class='n'>n</span><span class='p'>]</span> <span class='o'>==</span> <span class='mi'>0</span><span class='p'>:</span>
+            <span class='n'>data_masked</span><span class='p'>[</span><span class='n'>n</span><span class='p'>]</span> <span class='o'>=</span> <span class='mi'>0</span>
+        <span class='k'>else</span><span class='p'>:</span>
+            <span class='n'>data_masked</span><span class='p'>[</span><span class='n'>n</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>data</span><span class='p'>[</span><span class='n'>n</span><span class='p'>]</span>
+
+    <span class='n'>a</span> <span class='o'>=</span> <span class='p'>[]</span>
+    <span class='n'>b</span> <span class='o'>=</span> <span class='p'>[]</span>
+
+    <span class='k'>for</span> <span class='n'>bit</span><span class='p'>,</span> <span class='n'>r</span> <span class='ow'>in</span> <span class='n'>runs_of_non_zero</span><span class='p'>(</span><span class='n'>data_masked</span><span class='p'>):</span>
+        <span class='k'>if</span> <span class='n'>bit</span> <span class='o'>==</span> <span class='mi'>1</span><span class='p'>:</span>
+            <span class='n'>a</span><span class='o'>.</span><span class='n'>append</span><span class='p'>(</span><span class='nb'>list</span><span class='p'>(</span><span class='n'>r</span><span class='p'>))</span>
+        <span class='k'>else</span><span class='p'>:</span>
+            <span class='n'>b</span><span class='o'>.</span><span class='n'>append</span><span class='p'>(</span><span class='nb'>list</span><span class='p'>(</span><span class='n'>r</span><span class='p'>))</span>
+
+    <span class='k'>return</span> <span class='n'>a</span><span class='p'>,</span> <span class='n'>b</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+    <span class='n'>a</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>arange</span><span class='p'>(</span><span class='mi'>10</span><span class='p'>)</span>
+    <span class='n'>b</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>arange</span><span class='p'>(</span><span class='mi'>10</span><span class='p'>)</span>
+    <span class='nb'>print</span><span class='p'>(</span><span class='n'>mask_split_non_zero_concat</span><span class='p'>(</span><span class='n'>a</span><span class='p'>,</span> <span class='n'>b</span><span class='p'>))</span>
+
+    <span class='n'>c</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>zeros</span><span class='p'>(</span><span class='mi'>10</span><span class='p'>)</span>
+    <span class='n'>c</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:</span><span class='mi'>5</span><span class='p'>]</span> <span class='o'>=</span> <span class='mi'>1</span>
+    <span class='n'>c</span><span class='p'>[</span><span class='mi'>8</span><span class='p'>:</span><span class='mi'>10</span><span class='p'>]</span> <span class='o'>=</span> <span class='mi'>1</span>
+
+    <span class='n'>s</span> <span class='o'>=</span> <span class='nb'>range</span><span class='p'>(</span><span class='mi'>10</span><span class='p'>)</span>
+    <span class='nb'>print</span><span class='p'>(</span><span class='n'>mask_split_non_zero_segments</span><span class='p'>(</span><span class='n'>c</span><span class='p'>,</span> <span class='n'>s</span><span class='p'>))</span>
+</pre>
+            </div>
+
+          </div>
 
         </div>
       </div>
-      <div class="clearer"></div>
+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <p class='logo'><a href='../../../index.html'>
+            <img alt='Logo' class='logo' src='../../../_static/mod.svg'/>
+          </a></p>
+          <h1 class='logo'><a href='../../../index.html'>modulation</a></h1>
+
+
+          <h3>Navigation</h3>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../generated/modulation.html'>modulation</a></li>
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+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../../../index.html'>Documentation overview</a>
+                <ul>
+                  <li><a href='../../index.html'>Module code</a>
+                    <ul>
+                      <li><a href='../../modulation.html'>modulation</a>
+                        <ul>
+                        </ul>
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+                    </ul>
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+          <div id='searchbox' role='search' style='display: none'>
+            <h3 id='searchlabel'>Quick search</h3>
+            <div class='searchformwrapper'>
+              <form action='../../../search.html' class='search' method='get'>
+                <input aria-labelledby='searchlabel' autocapitalize='off' autocomplete='off' autocorrect='off' name='q' spellcheck='false' type='text'/>
+                <input type='submit' value='Go'/>
+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
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+      <div class='clearer'></div>
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+    <div class='footer'>
       &copy;.
-      
+
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-      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
-      
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
+
     </div>
 
-    
 
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diff --git a/docs/github/html/_modules/modulation/torch_utilities/collation.html b/docs/github/html/_modules/modulation/torch_utilities/collation.html
index 6b645f0..c1cc0e4 100644
--- a/docs/github/html/_modules/modulation/torch_utilities/collation.html
+++ b/docs/github/html/_modules/modulation/torch_utilities/collation.html
@@ -1,260 +1,254 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
     <title>modulation.torch_utilities.collation &#8212; modulation 0.0.1 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
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-    <link rel="canonical" href="Aivclab.github.io/modulation/_modules/modulation/torch_utilities/collation.html" />
-    <link rel="index" title="Index" href="../../../genindex.html" />
-    <link rel="search" title="Search" href="../../../search.html" />
-   
-  <link rel="stylesheet" href="../../../_static/custom.css" type="text/css" />
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-  
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-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          
-
-          <div class="body" role="main">
-            
-  <h1>Source code for modulation.torch_utilities.collation</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 03-12-2020</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">Tuple</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">draugr.torch_utilities</span> <span class="kn">import</span> <span class="n">global_torch_device</span>
-<span class="kn">from</span> <span class="nn">torch.types</span> <span class="kn">import</span> <span class="n">Device</span>
-
-<span class="kn">from</span> <span class="nn">modulation.torch_utilities.padding</span> <span class="kn">import</span> <span class="n">min_length_pad</span><span class="p">,</span> <span class="n">pad_sequence</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;collate_pad_wrapped&quot;</span><span class="p">,</span> <span class="s2">&quot;collate_transform_wrapped&quot;</span><span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span>
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">wave</span>
-
-
-<span class="c1"># shows the sound waves</span>
-<span class="k">def</span> <span class="nf">visualize</span><span class="p">(</span><span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param path:</span>
-<span class="sd">    :type path:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># reading the audio file</span>
-    <span class="n">raw</span> <span class="o">=</span> <span class="n">wave</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
-
-    <span class="c1"># reads all the frames</span>
-    <span class="c1"># -1 indicates all or max frames</span>
-    <span class="n">signal</span> <span class="o">=</span> <span class="n">raw</span><span class="o">.</span><span class="n">readframes</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">signal</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;int16&quot;</span><span class="p">)</span>
-
-    <span class="c1"># gets the frame rate</span>
-    <span class="n">f_rate</span> <span class="o">=</span> <span class="n">raw</span><span class="o">.</span><span class="n">getframerate</span><span class="p">()</span>
-
-    <span class="c1"># to Plot the x-axis in seconds</span>
-    <span class="c1"># you need get the frame rate</span>
-    <span class="c1"># and divide by size of your signal</span>
-    <span class="c1"># to create a Time Vector</span>
-    <span class="c1"># spaced linearly with the size</span>
-    <span class="c1"># of the audio file</span>
-    <span class="n">time</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">signal</span><span class="p">)</span> <span class="o">/</span> <span class="n">f_rate</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">signal</span><span class="p">))</span>
-
-    <span class="n">pyplot</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">pyplot</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Sound Wave&quot;</span><span class="p">)</span>
-    <span class="n">pyplot</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Time&quot;</span><span class="p">)</span>
-    <span class="n">pyplot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">signal</span><span class="p">)</span>
-    <span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-
-
-<div class="viewcode-block" id="collate_pad_wrapped"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.collation.collate_pad_wrapped.html#modulation.torch_utilities.collation.collate_pad_wrapped">[docs]</a><span class="k">def</span> <span class="nf">collate_pad_wrapped</span><span class="p">(</span><span class="n">device</span><span class="p">:</span> <span class="n">Device</span> <span class="o">=</span> <span class="n">global_torch_device</span><span class="p">())</span> <span class="o">-&gt;</span> <span class="n">callable</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param device:</span>
-<span class="sd">    :type device:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="nf">collate_fn</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Pads batch of variable length</span>
-
-<span class="sd">        returns batch, lengths, masks</span>
-
-<span class="sd">        note: it converts things ToTensor manually here since the ToTensor transform</span>
-<span class="sd">        assume it takes in images rather than arbitrary tensors.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">batch_</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">pad_sequence</span><span class="p">(</span>
-            <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">t</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>
-        <span class="p">)</span>
-
-        <span class="k">return</span> <span class="p">(</span>
-            <span class="n">batch_</span><span class="p">,</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
-                <span class="n">device</span>
-            <span class="p">),</span>  <span class="c1"># get original sequence lengths</span>
-            <span class="p">(</span><span class="n">batch_</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span>  <span class="c1"># compute mask</span>
-        <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">collate_fn</span></div>
-
-
-<div class="viewcode-block" id="collate_transform_wrapped"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.collation.collate_transform_wrapped.html#modulation.torch_utilities.collation.collate_transform_wrapped">[docs]</a><span class="k">def</span> <span class="nf">collate_transform_wrapped</span><span class="p">(</span>
-    <span class="n">mapping_func</span><span class="p">:</span> <span class="n">callable</span><span class="p">,</span>
-    <span class="n">transform</span><span class="p">:</span> <span class="n">callable</span><span class="p">,</span>
-    <span class="n">min_length</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">16000</span><span class="p">,</span>
-    <span class="n">device</span><span class="p">:</span> <span class="n">Device</span> <span class="o">=</span> <span class="n">global_torch_device</span><span class="p">(),</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">callable</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param mapping_func:</span>
-<span class="sd">    :type mapping_func:</span>
-<span class="sd">    :param transform:</span>
-<span class="sd">    :type transform:</span>
-<span class="sd">    :param min_length:</span>
-<span class="sd">    :type min_length:</span>
-<span class="sd">    :param device:</span>
-<span class="sd">    :type device:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    :rtype:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="nf">collate_fn</span><span class="p">(</span><span class="n">batch</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        To turn a list of data point made of audio recordings and utterances</span>
-<span class="sd">        into two batched tensors for the model, we implement a collate function</span>
-<span class="sd">        which is used by the PyTorch DataLoader that allows us to iterate over a</span>
-<span class="sd">        dataset by batches. Please see `the</span>
-<span class="sd">        documentation &lt;https://pytorch.org/docs/stable/data.html#working-with-collate-fn&gt;`__</span>
-<span class="sd">        for more information about working with a collate function.</span>
-
-<span class="sd">        In the collate function, we also apply the resampling, and the text</span>
-<span class="sd">        encoding.</span>
-
-
-<span class="sd">        # A data tuple has the form:</span>
-<span class="sd">        # waveform, sample_rate, label, speaker_id, utterance_number</span>
-
-<span class="sd">        :param batch:</span>
-<span class="sd">        :return:</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">tensors</span><span class="p">,</span> <span class="n">targets</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
-
-        <span class="k">for</span> <span class="p">(</span>
-            <span class="n">waveform</span><span class="p">,</span>
-            <span class="n">_</span><span class="p">,</span>
-            <span class="n">label</span><span class="p">,</span>
-            <span class="o">*</span><span class="n">_</span><span class="p">,</span>
-        <span class="p">)</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">:</span>  <span class="c1"># Gather in lists, and encode labels as indices</span>
-            <span class="n">tensors</span> <span class="o">+=</span> <span class="p">[</span><span class="n">min_length_pad</span><span class="p">(</span><span class="n">waveform</span><span class="p">,</span> <span class="n">min_length</span><span class="p">)]</span>
-            <span class="n">targets</span> <span class="o">+=</span> <span class="p">[</span><span class="n">mapping_func</span><span class="p">(</span><span class="n">label</span><span class="p">)]</span>
-
-        <span class="k">return</span> <span class="p">(</span>
-            <span class="n">transform</span><span class="p">(</span><span class="n">pad_sequence</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)),</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span>
-        <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">collate_fn</span></div>
-</pre></div>
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+            <h1>Source code for modulation.torch_utilities.collation</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 03-12-2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Iterable</span><span class='p'>,</span> <span class='n'>Tuple</span>
+
+<span class='kn'>import</span> <span class='nn'>torch</span>
+<span class='kn'>from</span> <span class='nn'>draugr.torch_utilities</span> <span class='kn'>import</span> <span class='n'>global_torch_device</span>
+<span class='kn'>from</span> <span class='nn'>torch.types</span> <span class='kn'>import</span> <span class='n'>Device</span>
+
+<span class='kn'>from</span> <span class='nn'>modulation.torch_utilities.padding</span> <span class='kn'>import</span> <span class='n'>min_length_pad</span><span class='p'>,</span> <span class='n'>pad_sequence</span>
+
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[</span><span class='s2'>&quot;collate_pad_wrapped&quot;</span><span class='p'>,</span> <span class='s2'>&quot;collate_transform_wrapped&quot;</span><span class='p'>]</span>
+
+<span class='kn'>from</span> <span class='nn'>matplotlib</span> <span class='kn'>import</span> <span class='n'>pyplot</span>
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>import</span> <span class='nn'>wave</span>
 
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-<li class="toctree-l1"><a class="reference internal" href="../../../getting_started.html">Getting Started</a></li>
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+<span class='c1'># shows the sound waves</span>
+<span class='k'>def</span> <span class='nf'>visualize</span><span class='p'>(</span><span class='n'>path</span><span class='p'>:</span> <span class='nb'>str</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
 
+<span class='sd'>    :param path:</span>
+<span class='sd'>    :type path:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='c1'># reading the audio file</span>
+    <span class='n'>raw</span> <span class='o'>=</span> <span class='n'>wave</span><span class='o'>.</span><span class='n'>open</span><span class='p'>(</span><span class='n'>path</span><span class='p'>)</span>
 
+    <span class='c1'># reads all the frames</span>
+    <span class='c1'># -1 indicates all or max frames</span>
+    <span class='n'>signal</span> <span class='o'>=</span> <span class='n'>raw</span><span class='o'>.</span><span class='n'>readframes</span><span class='p'>(</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>)</span>
+    <span class='n'>signal</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>frombuffer</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>,</span> <span class='n'>dtype</span><span class='o'>=</span><span class='s2'>&quot;int16&quot;</span><span class='p'>)</span>
 
+    <span class='c1'># gets the frame rate</span>
+    <span class='n'>f_rate</span> <span class='o'>=</span> <span class='n'>raw</span><span class='o'>.</span><span class='n'>getframerate</span><span class='p'>()</span>
 
+    <span class='c1'># to Plot the x-axis in seconds</span>
+    <span class='c1'># you need get the frame rate</span>
+    <span class='c1'># and divide by size of your signal</span>
+    <span class='c1'># to create a Time Vector</span>
+    <span class='c1'># spaced linearly with the size</span>
+    <span class='c1'># of the audio file</span>
+    <span class='n'>time</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>linspace</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>)</span> <span class='o'>/</span> <span class='n'>f_rate</span><span class='p'>,</span> <span class='n'>num</span><span class='o'>=</span><span class='nb'>len</span><span class='p'>(</span><span class='n'>signal</span><span class='p'>))</span>
+
+    <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>figure</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>)</span>
+    <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>title</span><span class='p'>(</span><span class='s2'>&quot;Sound Wave&quot;</span><span class='p'>)</span>
+    <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>xlabel</span><span class='p'>(</span><span class='s2'>&quot;Time&quot;</span><span class='p'>)</span>
+    <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>plot</span><span class='p'>(</span><span class='n'>time</span><span class='p'>,</span> <span class='n'>signal</span><span class='p'>)</span>
+    <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>show</span><span class='p'>()</span>
 
 
+<div class='viewcode-block' id='collate_pad_wrapped'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.collation.collate_pad_wrapped.html#modulation.torch_utilities.collation.collate_pad_wrapped'>[docs]</a><span class='k'>def</span> <span class='nf'>collate_pad_wrapped</span><span class='p'>(</span><span class='n'>device</span><span class='p'>:</span> <span class='n'>Device</span> <span class='o'>=</span> <span class='n'>global_torch_device</span><span class='p'>())</span> <span class='o'>-&gt;</span> <span class='n'>callable</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param device:</span>
+<span class='sd'>    :type device:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+    <span class='k'>def</span> <span class='nf'>collate_fn</span><span class='p'>(</span><span class='n'>batch</span><span class='p'>):</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>        Pads batch of variable length</span>
+
+<span class='sd'>        returns batch, lengths, masks</span>
+
+<span class='sd'>        note: it converts things ToTensor manually here since the ToTensor transform</span>
+<span class='sd'>        assume it takes in images rather than arbitrary tensors.</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+
+        <span class='n'>batch_</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>utils</span><span class='o'>.</span><span class='n'>rnn</span><span class='o'>.</span><span class='n'>pad_sequence</span><span class='p'>(</span>
+            <span class='p'>[</span><span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>(</span><span class='n'>t</span><span class='p'>)</span><span class='o'>.</span><span class='n'>to</span><span class='p'>(</span><span class='n'>device</span><span class='p'>)</span> <span class='k'>for</span> <span class='n'>t</span> <span class='ow'>in</span> <span class='n'>batch</span><span class='p'>]</span>
+        <span class='p'>)</span>
+
+        <span class='k'>return</span> <span class='p'>(</span>
+            <span class='n'>batch_</span><span class='p'>,</span>
+            <span class='n'>torch</span><span class='o'>.</span><span class='n'>tensor</span><span class='p'>([</span><span class='n'>t</span><span class='o'>.</span><span class='n'>shape</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>]</span> <span class='k'>for</span> <span class='n'>t</span> <span class='ow'>in</span> <span class='n'>batch</span><span class='p'>])</span><span class='o'>.</span><span class='n'>to</span><span class='p'>(</span>
+                <span class='n'>device</span>
+            <span class='p'>),</span>  <span class='c1'># get original sequence lengths</span>
+            <span class='p'>(</span><span class='n'>batch_</span> <span class='o'>!=</span> <span class='mi'>0</span><span class='p'>)</span><span class='o'>.</span><span class='n'>to</span><span class='p'>(</span><span class='n'>device</span><span class='p'>),</span>  <span class='c1'># compute mask</span>
+        <span class='p'>)</span>
+
+    <span class='k'>return</span> <span class='n'>collate_fn</span></div>
+
+
+<div class='viewcode-block' id='collate_transform_wrapped'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.collation.collate_transform_wrapped.html#modulation.torch_utilities.collation.collate_transform_wrapped'>[docs]</a><span class='k'>def</span> <span class='nf'>collate_transform_wrapped</span><span class='p'>(</span>
+    <span class='n'>mapping_func</span><span class='p'>:</span> <span class='n'>callable</span><span class='p'>,</span>
+    <span class='n'>transform</span><span class='p'>:</span> <span class='n'>callable</span><span class='p'>,</span>
+    <span class='n'>min_length</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='mi'>16000</span><span class='p'>,</span>
+    <span class='n'>device</span><span class='p'>:</span> <span class='n'>Device</span> <span class='o'>=</span> <span class='n'>global_torch_device</span><span class='p'>(),</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>callable</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param mapping_func:</span>
+<span class='sd'>    :type mapping_func:</span>
+<span class='sd'>    :param transform:</span>
+<span class='sd'>    :type transform:</span>
+<span class='sd'>    :param min_length:</span>
+<span class='sd'>    :type min_length:</span>
+<span class='sd'>    :param device:</span>
+<span class='sd'>    :type device:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    :rtype:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+    <span class='k'>def</span> <span class='nf'>collate_fn</span><span class='p'>(</span><span class='n'>batch</span><span class='p'>:</span> <span class='n'>Iterable</span><span class='p'>[</span><span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>])</span> <span class='o'>-&gt;</span> <span class='n'>Tuple</span><span class='p'>[</span><span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>,</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>]:</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>        To turn a list of data point made of audio recordings and utterances</span>
+<span class='sd'>        into two batched tensors for the model, we implement a collate function</span>
+<span class='sd'>        which is used by the PyTorch DataLoader that allows us to iterate over a</span>
+<span class='sd'>        dataset by batches. Please see `the</span>
+<span class='sd'>        documentation &lt;https://pytorch.org/docs/stable/data.html#working-with-collate-fn&gt;`__</span>
+<span class='sd'>        for more information about working with a collate function.</span>
+
+<span class='sd'>        In the collate function, we also apply the resampling, and the text</span>
+<span class='sd'>        encoding.</span>
+
+
+<span class='sd'>        # A data tuple has the form:</span>
+<span class='sd'>        # waveform, sample_rate, label, speaker_id, utterance_number</span>
+
+<span class='sd'>        :param batch:</span>
+<span class='sd'>        :return:</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+
+        <span class='n'>tensors</span><span class='p'>,</span> <span class='n'>targets</span> <span class='o'>=</span> <span class='p'>[],</span> <span class='p'>[]</span>
+
+        <span class='k'>for</span> <span class='p'>(</span>
+            <span class='n'>waveform</span><span class='p'>,</span>
+            <span class='n'>_</span><span class='p'>,</span>
+            <span class='n'>label</span><span class='p'>,</span>
+            <span class='o'>*</span><span class='n'>_</span><span class='p'>,</span>
+        <span class='p'>)</span> <span class='ow'>in</span> <span class='n'>batch</span><span class='p'>:</span>  <span class='c1'># Gather in lists, and encode labels as indices</span>
+            <span class='n'>tensors</span> <span class='o'>+=</span> <span class='p'>[</span><span class='n'>min_length_pad</span><span class='p'>(</span><span class='n'>waveform</span><span class='p'>,</span> <span class='n'>min_length</span><span class='p'>)]</span>
+            <span class='n'>targets</span> <span class='o'>+=</span> <span class='p'>[</span><span class='n'>mapping_func</span><span class='p'>(</span><span class='n'>label</span><span class='p'>)]</span>
+
+        <span class='k'>return</span> <span class='p'>(</span>
+            <span class='n'>transform</span><span class='p'>(</span><span class='n'>pad_sequence</span><span class='p'>(</span><span class='n'>tensors</span><span class='p'>)</span><span class='o'>.</span><span class='n'>to</span><span class='p'>(</span><span class='n'>device</span><span class='p'>)),</span>
+            <span class='n'>torch</span><span class='o'>.</span><span class='n'>stack</span><span class='p'>(</span><span class='n'>targets</span><span class='p'>)</span><span class='o'>.</span><span class='n'>to</span><span class='p'>(</span><span class='n'>device</span><span class='p'>),</span>
+        <span class='p'>)</span>
+
+    <span class='k'>return</span> <span class='n'>collate_fn</span></div>
+</pre>
+            </div>
+
+          </div>
 
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../getting_started.html'>Getting Started</a></li>
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+              <li><a href='../../../index.html'>Documentation overview</a>
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-  <h1>Source code for modulation.torch_utilities.padding</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian Heider Nielsen&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 01-12-2020</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">draugr.torch_utilities</span> <span class="kn">import</span> <span class="n">to_tensor</span>
-<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">functional</span>
-<span class="kn">from</span> <span class="nn">warg</span> <span class="kn">import</span> <span class="n">Number</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;batch_pad&quot;</span><span class="p">,</span> <span class="s2">&quot;pad_sequence&quot;</span><span class="p">,</span> <span class="s2">&quot;min_length_pad&quot;</span><span class="p">]</span>
-
-
-<div class="viewcode-block" id="batch_pad"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.padding.batch_pad.html#modulation.torch_utilities.padding.batch_pad">[docs]</a><span class="k">def</span> <span class="nf">batch_pad</span><span class="p">(</span><span class="n">image_batch</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">mask_batch</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Sequence</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">]:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param image_batch:</span>
-<span class="sd">    :param mask_batch:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Determine maximum height and width</span>
-    <span class="c1"># The mask&#39;s have the same height and width</span>
-    <span class="c1"># since they mask the image.</span>
-    <span class="n">max_height</span> <span class="o">=</span> <span class="nb">max</span><span class="p">([</span><span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">image_batch</span><span class="p">])</span>
-    <span class="n">max_width</span> <span class="o">=</span> <span class="nb">max</span><span class="p">([</span><span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">image_batch</span><span class="p">])</span>
-
-    <span class="n">image_batch</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="c1"># The needed padding is the difference between the</span>
-        <span class="c1"># max width/height and the image&#39;s actual width/height.</span>
-        <span class="n">functional</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">max_width</span> <span class="o">-</span> <span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="mi">0</span><span class="p">,</span> <span class="n">max_height</span> <span class="o">-</span> <span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)])</span>
-        <span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">image_batch</span>
-    <span class="p">]</span>
-    <span class="n">mask_batch</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="c1"># Same as for the images, but there is no channel dimension</span>
-        <span class="c1"># Therefore the mask&#39;s width is dimension 1 instead of 2</span>
-        <span class="n">functional</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span>
-            <span class="n">mask</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">max_width</span> <span class="o">-</span> <span class="n">mask</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="mi">0</span><span class="p">,</span> <span class="n">max_height</span> <span class="o">-</span> <span class="n">mask</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)]</span>
-        <span class="p">)</span>
-        <span class="k">for</span> <span class="n">mask</span> <span class="ow">in</span> <span class="n">mask_batch</span>
-    <span class="p">]</span>
-
-    <span class="k">return</span> <span class="n">image_batch</span><span class="p">,</span> <span class="n">mask_batch</span></div>
-
-
-<div class="viewcode-block" id="pad_sequence"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.padding.pad_sequence.html#modulation.torch_utilities.padding.pad_sequence">[docs]</a><span class="k">def</span> <span class="nf">pad_sequence</span><span class="p">(</span><span class="n">batch</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param batch:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">batch</span> <span class="o">=</span> <span class="p">[</span><span class="n">item</span><span class="o">.</span><span class="n">t</span><span class="p">()</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>  <span class="c1"># Transpose 2d</span>
-    <span class="n">batch</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">pad_sequence</span><span class="p">(</span>
-        <span class="n">batch</span><span class="p">,</span> <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">padding_value</span><span class="o">=</span><span class="mf">0.0</span>
-    <span class="p">)</span>  <span class="c1"># Make all tensor in a batch the same length by padding with zeros</span>
-    <span class="k">return</span> <span class="n">batch</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="min_length_pad"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.padding.min_length_pad.html#modulation.torch_utilities.padding.min_length_pad">[docs]</a><span class="k">def</span> <span class="nf">min_length_pad</span><span class="p">(</span>
-    <span class="n">sequence</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">min_length</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">padding_value</span><span class="p">:</span> <span class="n">Number</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param sequence:</span>
-<span class="sd">    :param min_length:</span>
-<span class="sd">    :param padding_value:</span>
-<span class="sd">    :return:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">out_dims</span> <span class="o">=</span> <span class="n">sequence</span><span class="o">.</span><span class="n">shape</span>
-    <span class="n">length</span> <span class="o">=</span> <span class="n">out_dims</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-    <span class="k">if</span> <span class="n">length</span> <span class="o">&lt;</span> <span class="n">min_length</span><span class="p">:</span>
-        <span class="n">out_dims</span> <span class="o">=</span> <span class="p">(</span><span class="o">*</span><span class="n">out_dims</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">min_length</span><span class="p">)</span>
-        <span class="n">out_tensor</span> <span class="o">=</span> <span class="n">sequence</span><span class="o">.</span><span class="n">new_full</span><span class="p">(</span>
-            <span class="n">out_dims</span><span class="p">,</span> <span class="n">padding_value</span>
-        <span class="p">)</span>  <span class="c1"># .data.new(*out_dims).fill_(padding_value)</span>
-        <span class="n">out_tensor</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">length</span><span class="p">]</span> <span class="o">=</span> <span class="n">sequence</span>
-        <span class="k">return</span> <span class="n">out_tensor</span>
-    <span class="k">return</span> <span class="n">sequence</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-
-    <span class="k">def</span> <span class="nf">a</span><span class="p">():</span>
-        <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-        <span class="n">base</span> <span class="o">=</span> <span class="mi">5</span>
-        <span class="n">stair_length</span> <span class="o">=</span> <span class="mi">9</span>
-        <span class="n">stair</span> <span class="o">=</span> <span class="p">[</span><span class="n">to_tensor</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="n">base</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">stair_length</span><span class="p">)]</span>
-
-        <span class="n">pad</span> <span class="o">=</span> <span class="p">[</span><span class="n">min_length_pad</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">stair_length</span> <span class="o">+</span> <span class="n">base</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">stair</span><span class="p">]</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">to_tensor</span><span class="p">(</span><span class="n">pad</span><span class="p">))</span>
-
-        <span class="n">a</span><span class="p">()</span>
-</pre></div>
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+            <h1>Source code for modulation.torch_utilities.padding</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian Heider Nielsen&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 01-12-2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Iterable</span><span class='p'>,</span> <span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>Tuple</span>
+
+<span class='kn'>import</span> <span class='nn'>torch</span>
+<span class='kn'>from</span> <span class='nn'>draugr.torch_utilities</span> <span class='kn'>import</span> <span class='n'>to_tensor</span>
+<span class='kn'>from</span> <span class='nn'>torch.nn</span> <span class='kn'>import</span> <span class='n'>functional</span>
+<span class='kn'>from</span> <span class='nn'>warg</span> <span class='kn'>import</span> <span class='n'>Number</span>
+
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[</span><span class='s2'>&quot;batch_pad&quot;</span><span class='p'>,</span> <span class='s2'>&quot;pad_sequence&quot;</span><span class='p'>,</span> <span class='s2'>&quot;min_length_pad&quot;</span><span class='p'>]</span>
+
+
+<div class='viewcode-block' id='batch_pad'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.padding.batch_pad.html#modulation.torch_utilities.padding.batch_pad'>[docs]</a><span class='k'>def</span> <span class='nf'>batch_pad</span><span class='p'>(</span><span class='n'>image_batch</span><span class='p'>:</span> <span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>mask_batch</span><span class='p'>:</span> <span class='n'>Sequence</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Tuple</span><span class='p'>[</span><span class='n'>Sequence</span><span class='p'>,</span> <span class='n'>Sequence</span><span class='p'>]:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param image_batch:</span>
+<span class='sd'>    :param mask_batch:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='c1'># Determine maximum height and width</span>
+    <span class='c1'># The mask&#39;s have the same height and width</span>
+    <span class='c1'># since they mask the image.</span>
+    <span class='n'>max_height</span> <span class='o'>=</span> <span class='nb'>max</span><span class='p'>([</span><span class='n'>img</span><span class='o'>.</span><span class='n'>size</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>)</span> <span class='k'>for</span> <span class='n'>img</span> <span class='ow'>in</span> <span class='n'>image_batch</span><span class='p'>])</span>
+    <span class='n'>max_width</span> <span class='o'>=</span> <span class='nb'>max</span><span class='p'>([</span><span class='n'>img</span><span class='o'>.</span><span class='n'>size</span><span class='p'>(</span><span class='mi'>2</span><span class='p'>)</span> <span class='k'>for</span> <span class='n'>img</span> <span class='ow'>in</span> <span class='n'>image_batch</span><span class='p'>])</span>
+
+    <span class='n'>image_batch</span> <span class='o'>=</span> <span class='p'>[</span>
+        <span class='c1'># The needed padding is the difference between the</span>
+        <span class='c1'># max width/height and the image&#39;s actual width/height.</span>
+        <span class='n'>functional</span><span class='o'>.</span><span class='n'>pad</span><span class='p'>(</span><span class='n'>img</span><span class='p'>,</span> <span class='p'>[</span><span class='mi'>0</span><span class='p'>,</span> <span class='n'>max_width</span> <span class='o'>-</span> <span class='n'>img</span><span class='o'>.</span><span class='n'>size</span><span class='p'>(</span><span class='mi'>2</span><span class='p'>),</span> <span class='mi'>0</span><span class='p'>,</span> <span class='n'>max_height</span> <span class='o'>-</span> <span class='n'>img</span><span class='o'>.</span><span class='n'>size</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>)])</span>
+        <span class='k'>for</span> <span class='n'>img</span> <span class='ow'>in</span> <span class='n'>image_batch</span>
+    <span class='p'>]</span>
+    <span class='n'>mask_batch</span> <span class='o'>=</span> <span class='p'>[</span>
+        <span class='c1'># Same as for the images, but there is no channel dimension</span>
+        <span class='c1'># Therefore the mask&#39;s width is dimension 1 instead of 2</span>
+        <span class='n'>functional</span><span class='o'>.</span><span class='n'>pad</span><span class='p'>(</span>
+            <span class='n'>mask</span><span class='p'>,</span> <span class='p'>[</span><span class='mi'>0</span><span class='p'>,</span> <span class='n'>max_width</span> <span class='o'>-</span> <span class='n'>mask</span><span class='o'>.</span><span class='n'>size</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>),</span> <span class='mi'>0</span><span class='p'>,</span> <span class='n'>max_height</span> <span class='o'>-</span> <span class='n'>mask</span><span class='o'>.</span><span class='n'>size</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>)]</span>
+        <span class='p'>)</span>
+        <span class='k'>for</span> <span class='n'>mask</span> <span class='ow'>in</span> <span class='n'>mask_batch</span>
+    <span class='p'>]</span>
+
+    <span class='k'>return</span> <span class='n'>image_batch</span><span class='p'>,</span> <span class='n'>mask_batch</span></div>
+
+
+<div class='viewcode-block' id='pad_sequence'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.padding.pad_sequence.html#modulation.torch_utilities.padding.pad_sequence'>[docs]</a><span class='k'>def</span> <span class='nf'>pad_sequence</span><span class='p'>(</span><span class='n'>batch</span><span class='p'>:</span> <span class='n'>Iterable</span><span class='p'>[</span><span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>])</span> <span class='o'>-&gt;</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param batch:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>batch</span> <span class='o'>=</span> <span class='p'>[</span><span class='n'>item</span><span class='o'>.</span><span class='n'>t</span><span class='p'>()</span> <span class='k'>for</span> <span class='n'>item</span> <span class='ow'>in</span> <span class='n'>batch</span><span class='p'>]</span>  <span class='c1'># Transpose 2d</span>
+    <span class='n'>batch</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>utils</span><span class='o'>.</span><span class='n'>rnn</span><span class='o'>.</span><span class='n'>pad_sequence</span><span class='p'>(</span>
+        <span class='n'>batch</span><span class='p'>,</span> <span class='n'>batch_first</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>,</span> <span class='n'>padding_value</span><span class='o'>=</span><span class='mf'>0.0</span>
+    <span class='p'>)</span>  <span class='c1'># Make all tensor in a batch the same length by padding with zeros</span>
+    <span class='k'>return</span> <span class='n'>batch</span><span class='o'>.</span><span class='n'>permute</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>,</span> <span class='mi'>2</span><span class='p'>,</span> <span class='mi'>1</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='min_length_pad'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.padding.min_length_pad.html#modulation.torch_utilities.padding.min_length_pad'>[docs]</a><span class='k'>def</span> <span class='nf'>min_length_pad</span><span class='p'>(</span>
+    <span class='n'>sequence</span><span class='p'>:</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>,</span> <span class='n'>min_length</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>,</span> <span class='n'>padding_value</span><span class='p'>:</span> <span class='n'>Number</span> <span class='o'>=</span> <span class='mi'>0</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>    :param sequence:</span>
+<span class='sd'>    :param min_length:</span>
+<span class='sd'>    :param padding_value:</span>
+<span class='sd'>    :return:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>out_dims</span> <span class='o'>=</span> <span class='n'>sequence</span><span class='o'>.</span><span class='n'>shape</span>
+    <span class='n'>length</span> <span class='o'>=</span> <span class='n'>out_dims</span><span class='p'>[</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>]</span>
+    <span class='k'>if</span> <span class='n'>length</span> <span class='o'>&lt;</span> <span class='n'>min_length</span><span class='p'>:</span>
+        <span class='n'>out_dims</span> <span class='o'>=</span> <span class='p'>(</span><span class='o'>*</span><span class='n'>out_dims</span><span class='p'>[:</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>],</span> <span class='n'>min_length</span><span class='p'>)</span>
+        <span class='n'>out_tensor</span> <span class='o'>=</span> <span class='n'>sequence</span><span class='o'>.</span><span class='n'>new_full</span><span class='p'>(</span>
+            <span class='n'>out_dims</span><span class='p'>,</span> <span class='n'>padding_value</span>
+        <span class='p'>)</span>  <span class='c1'># .data.new(*out_dims).fill_(padding_value)</span>
+        <span class='n'>out_tensor</span><span class='p'>[</span><span class='o'>...</span><span class='p'>,</span> <span class='p'>:</span><span class='n'>length</span><span class='p'>]</span> <span class='o'>=</span> <span class='n'>sequence</span>
+        <span class='k'>return</span> <span class='n'>out_tensor</span>
+    <span class='k'>return</span> <span class='n'>sequence</span></div>
 
 
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
 
+    <span class='k'>def</span> <span class='nf'>a</span><span class='p'>():</span>
+        <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+        <span class='n'>base</span> <span class='o'>=</span> <span class='mi'>5</span>
+        <span class='n'>stair_length</span> <span class='o'>=</span> <span class='mi'>9</span>
+        <span class='n'>stair</span> <span class='o'>=</span> <span class='p'>[</span><span class='n'>to_tensor</span><span class='p'>(</span><span class='nb'>range</span><span class='p'>(</span><span class='n'>i</span> <span class='o'>+</span> <span class='n'>base</span><span class='p'>))</span> <span class='k'>for</span> <span class='n'>i</span> <span class='ow'>in</span> <span class='nb'>range</span><span class='p'>(</span><span class='n'>stair_length</span><span class='p'>)]</span>
+
+        <span class='n'>pad</span> <span class='o'>=</span> <span class='p'>[</span><span class='n'>min_length_pad</span><span class='p'>(</span><span class='n'>s</span><span class='p'>,</span> <span class='n'>stair_length</span> <span class='o'>+</span> <span class='n'>base</span><span class='p'>)</span> <span class='k'>for</span> <span class='n'>s</span> <span class='ow'>in</span> <span class='n'>stair</span><span class='p'>]</span>
+        <span class='nb'>print</span><span class='p'>(</span><span class='n'>to_tensor</span><span class='p'>(</span><span class='n'>pad</span><span class='p'>))</span>
+
+        <span class='n'>a</span><span class='p'>()</span>
+</pre>
+            </div>
 
+          </div>
 
+        </div>
+      </div>
+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <p class='logo'><a href='../../../index.html'>
+            <img alt='Logo' class='logo' src='../../../_static/mod.svg'/>
+          </a></p>
+          <h1 class='logo'><a href='../../../index.html'>modulation</a></h1>
+
+
+          <h3>Navigation</h3>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../generated/modulation.html'>modulation</a></li>
+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../../../index.html'>Documentation overview</a>
+                <ul>
+                  <li><a href='../../index.html'>Module code</a>
+                    <ul>
+                      <li><a href='../../modulation.html'>modulation</a>
+                        <ul>
+                        </ul>
+                      </li>
+                    </ul>
+                  </li>
+                </ul>
+              </li>
+            </ul>
+          </div>
+          <div id='searchbox' role='search' style='display: none'>
+            <h3 id='searchlabel'>Quick search</h3>
+            <div class='searchformwrapper'>
+              <form action='../../../search.html' class='search' method='get'>
+                <input aria-labelledby='searchlabel' autocapitalize='off' autocomplete='off' autocorrect='off' name='q' spellcheck='false' type='text'/>
+                <input type='submit' value='Go'/>
+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
 
 
         </div>
       </div>
-      <div class="clearer"></div>
+      <div class='clearer'></div>
     </div>
-    <div class="footer">
+    <div class='footer'>
       &copy;.
-      
+
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
-      
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
+
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-    
 
-    
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 </html>
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diff --git a/docs/github/html/_modules/modulation/torch_utilities/torch_transforms.html b/docs/github/html/_modules/modulation/torch_utilities/torch_transforms.html
index 800166f..71056cf 100644
--- a/docs/github/html/_modules/modulation/torch_utilities/torch_transforms.html
+++ b/docs/github/html/_modules/modulation/torch_utilities/torch_transforms.html
@@ -1,345 +1,339 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
     <title>modulation.torch_utilities.torch_transforms &#8212; modulation 0.0.1 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/alabaster.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/graphviz.css" />
-    <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
-    <script src="../../../_static/jquery.js"></script>
-    <script src="../../../_static/underscore.js"></script>
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-    <script src="../../../_static/doctools.js"></script>
-    <link rel="canonical" href="Aivclab.github.io/modulation/_modules/modulation/torch_utilities/torch_transforms.html" />
-    <link rel="index" title="Index" href="../../../genindex.html" />
-    <link rel="search" title="Search" href="../../../search.html" />
-   
-  <link rel="stylesheet" href="../../../_static/custom.css" type="text/css" />
-  
-  
-  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
-
-  </head><body>
-  
-
-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          
-
-          <div class="body" role="main">
-            
-  <h1>Source code for modulation.torch_utilities.torch_transforms</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">warnings</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>
-
-<span class="kn">import</span> <span class="nn">librosa</span>
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">torchaudio</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-<span class="kn">from</span> <span class="nn">torch.nn.functional</span> <span class="kn">import</span> <span class="n">conv1d</span><span class="p">,</span> <span class="n">pad</span>
-<span class="kn">from</span> <span class="nn">warg</span> <span class="kn">import</span> <span class="n">passes_kws_to</span>
-
-
-<div class="viewcode-block" id="PreEmphasis"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.PreEmphasis.html#modulation.torch_utilities.torch_transforms.PreEmphasis">[docs]</a><span class="k">class</span> <span class="nc">PreEmphasis</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="PreEmphasis.__init__"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.PreEmphasis.html#modulation.torch_utilities.torch_transforms.PreEmphasis.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">coef</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.97</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">coef</span> <span class="o">=</span> <span class="n">coef</span>
-        <span class="c1"># make kernel</span>
-        <span class="c1"># In pytorch, the convolution operation uses cross-correlation. So, filter is flipped.</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span>
-            <span class="s2">&quot;flipped_filter&quot;</span><span class="p">,</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">coef</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span>
-        <span class="p">)</span></div>
-
-<div class="viewcode-block" id="PreEmphasis.forward"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.PreEmphasis.html#modulation.torch_utilities.torch_transforms.PreEmphasis.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">        :param x:</span>
-<span class="sd">        :type x:</span>
-<span class="sd">        :return:</span>
-<span class="sd">        :rtype:</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">())</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;The number of dimensions of input tensor must be 3!&quot;</span>
-        <span class="c1"># reflect padding to match lengths of in/out</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">pad</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="s2">&quot;reflect&quot;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">conv1d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">flipped_filter</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="InversePreEmphasis"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.InversePreEmphasis.html#modulation.torch_utilities.torch_transforms.InversePreEmphasis">[docs]</a><span class="k">class</span> <span class="nc">InversePreEmphasis</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Implement Inverse Pre-emphasis by using RNN to boost up inference speed.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="InversePreEmphasis.__init__"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.InversePreEmphasis.html#modulation.torch_utilities.torch_transforms.InversePreEmphasis.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">coef</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.97</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">coef</span> <span class="o">=</span> <span class="n">coef</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">RNN</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="c1"># use originally on that time</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">weight_ih_l0</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-        <span class="c1"># multiply coefficient on previous output</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span><span class="o">.</span><span class="n">weight_hh_l0</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coef</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="InversePreEmphasis.forward"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.InversePreEmphasis.html#modulation.torch_utilities.torch_transforms.InversePreEmphasis.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">        :param x:</span>
-<span class="sd">        :type x:</span>
-<span class="sd">        :return:</span>
-<span class="sd">        :rtype:</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">x</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="MelSpectrogram"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.MelSpectrogram.html#modulation.torch_utilities.torch_transforms.MelSpectrogram">[docs]</a><span class="k">class</span> <span class="nc">MelSpectrogram</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    torchaudio MelSpectrogram wrapper for audiomentations&#39;s Compose</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="MelSpectrogram.__init__"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.MelSpectrogram.html#modulation.torch_utilities.torch_transforms.MelSpectrogram.__init__">[docs]</a>    <span class="nd">@passes_kws_to</span><span class="p">(</span><span class="n">torchaudio</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">MelSpectrogram</span><span class="p">)</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">clip_min_value</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">n_fft</span><span class="p">,</span> <span class="n">n_mels</span><span class="p">,</span> <span class="n">f_min</span><span class="p">,</span> <span class="n">f_max</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">torchaudio</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">MelSpectrogram</span><span class="p">(</span>
-            <span class="n">sample_rate</span><span class="p">,</span> <span class="n">n_fft</span><span class="p">,</span> <span class="n">n_mels</span><span class="p">,</span> <span class="n">f_min</span><span class="p">,</span> <span class="n">f_max</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">clip_min_value</span> <span class="o">=</span> <span class="n">clip_min_value</span>
-
-        <span class="n">mel_basis</span> <span class="o">=</span> <span class="n">librosa</span><span class="o">.</span><span class="n">filters</span><span class="o">.</span><span class="n">mel</span><span class="p">(</span>
-            <span class="n">sr</span><span class="o">=</span><span class="n">sample_rate</span><span class="p">,</span>
-            <span class="n">n_fft</span><span class="o">=</span><span class="n">n_fft</span><span class="p">,</span>
-            <span class="n">n_mels</span><span class="o">=</span><span class="n">n_mels</span><span class="p">,</span>
-            <span class="n">fmin</span><span class="o">=</span><span class="n">f_min</span><span class="p">,</span>
-            <span class="n">fmax</span><span class="o">=</span><span class="n">f_max</span><span class="p">,</span>
-        <span class="p">)</span><span class="o">.</span><span class="n">T</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">mel_scale</span><span class="o">.</span><span class="n">fb</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">mel_basis</span><span class="p">))</span></div>
-
-<div class="viewcode-block" id="MelSpectrogram.forward"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.MelSpectrogram.html#modulation.torch_utilities.torch_transforms.MelSpectrogram.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">sample_rate</span><span class="p">:</span> <span class="nb">int</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">        :param samples:</span>
-<span class="sd">        :type samples:</span>
-<span class="sd">        :param sample_rate:</span>
-<span class="sd">        :type sample_rate:</span>
-<span class="sd">        :return:</span>
-<span class="sd">        :rtype:</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
-            <span class="n">samples</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
-        <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">():</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
-            <span class="n">samples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
-        <span class="n">samples</span><span class="o">.</span><span class="n">clamp_</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">clip_min_value</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">samples</span></div></div>
-
-
-<div class="viewcode-block" id="Spectrogram"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.Spectrogram.html#modulation.torch_utilities.torch_transforms.Spectrogram">[docs]</a><span class="k">class</span> <span class="nc">Spectrogram</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Apply stft and magphase transformations</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="Spectrogram.__init__"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.Spectrogram.html#modulation.torch_utilities.torch_transforms.Spectrogram.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_fft</span><span class="p">,</span> <span class="n">win_length</span><span class="p">,</span> <span class="n">hop_length</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_fft</span> <span class="o">=</span> <span class="n">n_fft</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">win_length</span> <span class="o">=</span> <span class="n">win_length</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">hop_length</span> <span class="o">=</span> <span class="n">hop_length</span></div>
-
-<div class="viewcode-block" id="Spectrogram.forward"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.Spectrogram.html#modulation.torch_utilities.torch_transforms.Spectrogram.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">sample_rate</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Apply transfrom</span>
-<span class="sd">        :return: two tensors</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">():</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
-            <span class="n">spec</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stft</span><span class="p">(</span>
-                <span class="n">samples</span><span class="p">,</span>
-                <span class="n">n_fft</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_fft</span><span class="p">,</span>
-                <span class="n">win_length</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">win_length</span><span class="p">,</span>
-                <span class="n">hop_length</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">hop_length</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="n">mag</span><span class="p">,</span> <span class="n">phase</span> <span class="o">=</span> <span class="n">torchaudio</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">magphase</span><span class="p">(</span><span class="n">spec</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">mag</span><span class="p">,</span> <span class="n">phase</span></div></div>
-
-
-<div class="viewcode-block" id="InverseSpectrogram"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.InverseSpectrogram.html#modulation.torch_utilities.torch_transforms.InverseSpectrogram">[docs]</a><span class="k">class</span> <span class="nc">InverseSpectrogram</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Convert from magphase to complex and perform istft</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="InverseSpectrogram.__init__"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.InverseSpectrogram.html#modulation.torch_utilities.torch_transforms.InverseSpectrogram.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_fft</span><span class="p">,</span> <span class="n">win_length</span><span class="p">,</span> <span class="n">hop_length</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_fft</span> <span class="o">=</span> <span class="n">n_fft</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">win_length</span> <span class="o">=</span> <span class="n">win_length</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">hop_length</span> <span class="o">=</span> <span class="n">hop_length</span></div>
-
-<div class="viewcode-block" id="InverseSpectrogram.forward"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.InverseSpectrogram.html#modulation.torch_utilities.torch_transforms.InverseSpectrogram.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">sample_rate</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">        :param samples:</span>
-<span class="sd">        :type samples:</span>
-<span class="sd">        :param sample_rate:</span>
-<span class="sd">        :type sample_rate:</span>
-<span class="sd">        :return:</span>
-<span class="sd">        :rtype:</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">mag</span><span class="p">,</span> <span class="n">phase</span> <span class="o">=</span> <span class="n">samples</span>
-        <span class="n">spec</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">phase</span><span class="p">)</span> <span class="o">*</span> <span class="n">mag</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">phase</span><span class="p">)</span> <span class="o">*</span> <span class="n">mag</span><span class="p">],</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">():</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">)</span>
-            <span class="n">signal</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">istft</span><span class="p">(</span>
-                <span class="n">spec</span><span class="p">,</span>
-                <span class="n">n_fft</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_fft</span><span class="p">,</span>
-                <span class="n">win_length</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">win_length</span><span class="p">,</span>
-                <span class="n">hop_length</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">hop_length</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="k">return</span> <span class="n">signal</span></div></div>
-
-
-<div class="viewcode-block" id="ToMono"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.ToMono.html#modulation.torch_utilities.torch_transforms.ToMono">[docs]</a><span class="k">class</span> <span class="nc">ToMono</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Convert stereo signal to mono</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">sample_rate</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">        :param samples:</span>
-<span class="sd">        :param sample_rate: dummy parameter for compatibility</span>
-<span class="sd">        :return:</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="Squeeze"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.Squeeze.html#modulation.torch_utilities.torch_transforms.Squeeze">[docs]</a><span class="k">class</span> <span class="nc">Squeeze</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Transform to squeeze mono channel waveform</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">sample_rate</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">samples</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="ToNumpy"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.ToNumpy.html#modulation.torch_utilities.torch_transforms.ToNumpy">[docs]</a><span class="k">class</span> <span class="nc">ToNumpy</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Transform to make numpy array</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">sample_rate</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="ToTorch"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.ToTorch.html#modulation.torch_utilities.torch_transforms.ToTorch">[docs]</a><span class="k">class</span> <span class="nc">ToTorch</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Transform to make torch.tensor</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">sample_rate</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="LogTransform"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.LogTransform.html#modulation.torch_utilities.torch_transforms.LogTransform">[docs]</a><span class="k">class</span> <span class="nc">LogTransform</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Transform for taking logarithm of mel spectrograms (or anything else)</span>
-<span class="sd">    :param fill_value: value to substitute non-positive numbers with before applying log</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="LogTransform.__init__"><a class="viewcode-back" href="../../../generated/modulation.torch_utilities.torch_transforms.LogTransform.html#modulation.torch_utilities.torch_transforms.LogTransform.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fill_value</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-5</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fill_value</span> <span class="o">=</span> <span class="n">fill_value</span></div>
-
-    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-        <span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span><span class="o">.</span><span class="n">masked_fill</span><span class="p">((</span><span class="n">samples</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">fill_value</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span></div>
-</pre></div>
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+
+            <h1>Source code for modulation.torch_utilities.torch_transforms</h1>
+            <div class='highlight'><pre>
+<span></span><span class='kn'>import</span> <span class='nn'>warnings</span>
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Tuple</span><span class='p'>,</span> <span class='n'>Union</span>
+
+<span class='kn'>import</span> <span class='nn'>librosa</span>
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>import</span> <span class='nn'>torch</span>
+<span class='kn'>import</span> <span class='nn'>torchaudio</span>
+<span class='kn'>from</span> <span class='nn'>torch</span> <span class='kn'>import</span> <span class='n'>nn</span>
+<span class='kn'>from</span> <span class='nn'>torch.nn.functional</span> <span class='kn'>import</span> <span class='n'>conv1d</span><span class='p'>,</span> <span class='n'>pad</span>
+<span class='kn'>from</span> <span class='nn'>warg</span> <span class='kn'>import</span> <span class='n'>passes_kws_to</span>
+
+
+<div class='viewcode-block' id='PreEmphasis'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.PreEmphasis.html#modulation.torch_utilities.torch_transforms.PreEmphasis'>[docs]</a><span class='k'>class</span> <span class='nc'>PreEmphasis</span><span class='p'>(</span><span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Module</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+
+<div class='viewcode-block' id='PreEmphasis.__init__'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.PreEmphasis.html#modulation.torch_utilities.torch_transforms.PreEmphasis.__init__'>[docs]</a>    <span class='k'>def</span> <span class='fm'>__init__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>coef</span><span class='p'>:</span> <span class='nb'>float</span> <span class='o'>=</span> <span class='mf'>0.97</span><span class='p'>):</span>
+        <span class='nb'>super</span><span class='p'>()</span><span class='o'>.</span><span class='fm'>__init__</span><span class='p'>()</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>coef</span> <span class='o'>=</span> <span class='n'>coef</span>
+        <span class='c1'># make kernel</span>
+        <span class='c1'># In pytorch, the convolution operation uses cross-correlation. So, filter is flipped.</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>register_buffer</span><span class='p'>(</span>
+            <span class='s2'>&quot;flipped_filter&quot;</span><span class='p'>,</span>
+            <span class='n'>torch</span><span class='o'>.</span><span class='n'>FloatTensor</span><span class='p'>([</span><span class='o'>-</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>coef</span><span class='p'>,</span> <span class='mf'>1.0</span><span class='p'>])</span><span class='o'>.</span><span class='n'>unsqueeze</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>)</span><span class='o'>.</span><span class='n'>unsqueeze</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>),</span>
+        <span class='p'>)</span></div>
+
+<div class='viewcode-block' id='PreEmphasis.forward'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.PreEmphasis.html#modulation.torch_utilities.torch_transforms.PreEmphasis.forward'>[docs]</a>    <span class='k'>def</span> <span class='nf'>forward</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>x</span><span class='p'>:</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>tensor</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>tensor</span><span class='p'>:</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>        :param x:</span>
+<span class='sd'>        :type x:</span>
+<span class='sd'>        :return:</span>
+<span class='sd'>        :rtype:</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='k'>assert</span> <span class='nb'>len</span><span class='p'>(</span><span class='n'>x</span><span class='o'>.</span><span class='n'>size</span><span class='p'>())</span> <span class='o'>==</span> <span class='mi'>3</span><span class='p'>,</span> <span class='s2'>&quot;The number of dimensions of input tensor must be 3!&quot;</span>
+        <span class='c1'># reflect padding to match lengths of in/out</span>
+        <span class='n'>x</span> <span class='o'>=</span> <span class='n'>pad</span><span class='p'>(</span><span class='n'>x</span><span class='p'>,</span> <span class='p'>(</span><span class='mi'>1</span><span class='p'>,</span> <span class='mi'>0</span><span class='p'>),</span> <span class='s2'>&quot;reflect&quot;</span><span class='p'>)</span>
+        <span class='k'>return</span> <span class='n'>conv1d</span><span class='p'>(</span><span class='n'>x</span><span class='p'>,</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>flipped_filter</span><span class='p'>)</span></div></div>
+
+
+<div class='viewcode-block' id='InversePreEmphasis'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.InversePreEmphasis.html#modulation.torch_utilities.torch_transforms.InversePreEmphasis'>[docs]</a><span class='k'>class</span> <span class='nc'>InversePreEmphasis</span><span class='p'>(</span><span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Module</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    Implement Inverse Pre-emphasis by using RNN to boost up inference speed.</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+<div class='viewcode-block' id='InversePreEmphasis.__init__'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.InversePreEmphasis.html#modulation.torch_utilities.torch_transforms.InversePreEmphasis.__init__'>[docs]</a>    <span class='k'>def</span> <span class='fm'>__init__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>coef</span><span class='p'>:</span> <span class='nb'>float</span> <span class='o'>=</span> <span class='mf'>0.97</span><span class='p'>):</span>
+        <span class='nb'>super</span><span class='p'>()</span><span class='o'>.</span><span class='fm'>__init__</span><span class='p'>()</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>coef</span> <span class='o'>=</span> <span class='n'>coef</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>rnn</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>RNN</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>,</span> <span class='mi'>1</span><span class='p'>,</span> <span class='mi'>1</span><span class='p'>,</span> <span class='n'>bias</span><span class='o'>=</span><span class='kc'>False</span><span class='p'>,</span> <span class='n'>batch_first</span><span class='o'>=</span><span class='kc'>True</span><span class='p'>)</span>
+        <span class='c1'># use originally on that time</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>rnn</span><span class='o'>.</span><span class='n'>weight_ih_l0</span><span class='o'>.</span><span class='n'>data</span><span class='o'>.</span><span class='n'>fill_</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>)</span>
+        <span class='c1'># multiply coefficient on previous output</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>rnn</span><span class='o'>.</span><span class='n'>weight_hh_l0</span><span class='o'>.</span><span class='n'>data</span><span class='o'>.</span><span class='n'>fill_</span><span class='p'>(</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>coef</span><span class='p'>)</span></div>
+
+<div class='viewcode-block' id='InversePreEmphasis.forward'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.InversePreEmphasis.html#modulation.torch_utilities.torch_transforms.InversePreEmphasis.forward'>[docs]</a>    <span class='k'>def</span> <span class='nf'>forward</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>x</span><span class='p'>:</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>tensor</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>tensor</span><span class='p'>:</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>        :param x:</span>
+<span class='sd'>        :type x:</span>
+<span class='sd'>        :return:</span>
+<span class='sd'>        :rtype:</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='n'>x</span><span class='p'>,</span> <span class='n'>_</span> <span class='o'>=</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>rnn</span><span class='p'>(</span><span class='n'>x</span><span class='o'>.</span><span class='n'>transpose</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>,</span> <span class='mi'>2</span><span class='p'>))</span>
+        <span class='k'>return</span> <span class='n'>x</span><span class='o'>.</span><span class='n'>transpose</span><span class='p'>(</span><span class='mi'>1</span><span class='p'>,</span> <span class='mi'>2</span><span class='p'>)</span></div></div>
+
+
+<div class='viewcode-block' id='MelSpectrogram'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.MelSpectrogram.html#modulation.torch_utilities.torch_transforms.MelSpectrogram'>[docs]</a><span class='k'>class</span> <span class='nc'>MelSpectrogram</span><span class='p'>(</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Module</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    torchaudio MelSpectrogram wrapper for audiomentations&#39;s Compose</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+<div class='viewcode-block' id='MelSpectrogram.__init__'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.MelSpectrogram.html#modulation.torch_utilities.torch_transforms.MelSpectrogram.__init__'>[docs]</a>    <span class='nd'>@passes_kws_to</span><span class='p'>(</span><span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>transforms</span><span class='o'>.</span><span class='n'>MelSpectrogram</span><span class='p'>)</span>
+    <span class='k'>def</span> <span class='fm'>__init__</span><span class='p'>(</span>
+        <span class='bp'>self</span><span class='p'>,</span> <span class='n'>clip_min_value</span><span class='o'>=</span><span class='mf'>1e-5</span><span class='p'>,</span> <span class='o'>*</span><span class='p'>,</span> <span class='n'>sample_rate</span><span class='p'>,</span> <span class='n'>n_fft</span><span class='p'>,</span> <span class='n'>n_mels</span><span class='p'>,</span> <span class='n'>f_min</span><span class='p'>,</span> <span class='n'>f_max</span><span class='p'>,</span> <span class='o'>**</span><span class='n'>kwargs</span>
+    <span class='p'>):</span>
+        <span class='nb'>super</span><span class='p'>()</span><span class='o'>.</span><span class='fm'>__init__</span><span class='p'>()</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>transform</span> <span class='o'>=</span> <span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>transforms</span><span class='o'>.</span><span class='n'>MelSpectrogram</span><span class='p'>(</span>
+            <span class='n'>sample_rate</span><span class='p'>,</span> <span class='n'>n_fft</span><span class='p'>,</span> <span class='n'>n_mels</span><span class='p'>,</span> <span class='n'>f_min</span><span class='p'>,</span> <span class='n'>f_max</span><span class='p'>,</span> <span class='o'>**</span><span class='n'>kwargs</span>
+        <span class='p'>)</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>clip_min_value</span> <span class='o'>=</span> <span class='n'>clip_min_value</span>
+
+        <span class='n'>mel_basis</span> <span class='o'>=</span> <span class='n'>librosa</span><span class='o'>.</span><span class='n'>filters</span><span class='o'>.</span><span class='n'>mel</span><span class='p'>(</span>
+            <span class='n'>sr</span><span class='o'>=</span><span class='n'>sample_rate</span><span class='p'>,</span>
+            <span class='n'>n_fft</span><span class='o'>=</span><span class='n'>n_fft</span><span class='p'>,</span>
+            <span class='n'>n_mels</span><span class='o'>=</span><span class='n'>n_mels</span><span class='p'>,</span>
+            <span class='n'>fmin</span><span class='o'>=</span><span class='n'>f_min</span><span class='p'>,</span>
+            <span class='n'>fmax</span><span class='o'>=</span><span class='n'>f_max</span><span class='p'>,</span>
+        <span class='p'>)</span><span class='o'>.</span><span class='n'>T</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>transform</span><span class='o'>.</span><span class='n'>mel_scale</span><span class='o'>.</span><span class='n'>fb</span><span class='o'>.</span><span class='n'>copy_</span><span class='p'>(</span><span class='n'>torch</span><span class='o'>.</span><span class='n'>tensor</span><span class='p'>(</span><span class='n'>mel_basis</span><span class='p'>))</span></div>
+
+<div class='viewcode-block' id='MelSpectrogram.forward'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.MelSpectrogram.html#modulation.torch_utilities.torch_transforms.MelSpectrogram.forward'>[docs]</a>    <span class='k'>def</span> <span class='nf'>forward</span><span class='p'>(</span>
+        <span class='bp'>self</span><span class='p'>,</span> <span class='n'>samples</span><span class='p'>:</span> <span class='n'>Union</span><span class='p'>[</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>],</span> <span class='n'>sample_rate</span><span class='p'>:</span> <span class='nb'>int</span>
+    <span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>:</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>        :param samples:</span>
+<span class='sd'>        :type samples:</span>
+<span class='sd'>        :param sample_rate:</span>
+<span class='sd'>        :type sample_rate:</span>
+<span class='sd'>        :return:</span>
+<span class='sd'>        :rtype:</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='k'>if</span> <span class='ow'>not</span> <span class='nb'>isinstance</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>,</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>):</span>
+            <span class='n'>samples</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>tensor</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>)</span>
+        <span class='k'>with</span> <span class='n'>warnings</span><span class='o'>.</span><span class='n'>catch_warnings</span><span class='p'>():</span>
+            <span class='n'>warnings</span><span class='o'>.</span><span class='n'>simplefilter</span><span class='p'>(</span><span class='s2'>&quot;ignore&quot;</span><span class='p'>)</span>
+            <span class='n'>samples</span> <span class='o'>=</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>transform</span><span class='o'>.</span><span class='n'>forward</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>)</span>
+        <span class='n'>samples</span><span class='o'>.</span><span class='n'>clamp_</span><span class='p'>(</span><span class='nb'>min</span><span class='o'>=</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>clip_min_value</span><span class='p'>)</span>
+        <span class='k'>return</span> <span class='n'>samples</span></div></div>
+
+
+<div class='viewcode-block' id='Spectrogram'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.Spectrogram.html#modulation.torch_utilities.torch_transforms.Spectrogram'>[docs]</a><span class='k'>class</span> <span class='nc'>Spectrogram</span><span class='p'>(</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Module</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    Apply stft and magphase transformations</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+<div class='viewcode-block' id='Spectrogram.__init__'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.Spectrogram.html#modulation.torch_utilities.torch_transforms.Spectrogram.__init__'>[docs]</a>    <span class='k'>def</span> <span class='fm'>__init__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>n_fft</span><span class='p'>,</span> <span class='n'>win_length</span><span class='p'>,</span> <span class='n'>hop_length</span><span class='p'>):</span>
+        <span class='nb'>super</span><span class='p'>()</span><span class='o'>.</span><span class='fm'>__init__</span><span class='p'>()</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>n_fft</span> <span class='o'>=</span> <span class='n'>n_fft</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>win_length</span> <span class='o'>=</span> <span class='n'>win_length</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>hop_length</span> <span class='o'>=</span> <span class='n'>hop_length</span></div>
+
+<div class='viewcode-block' id='Spectrogram.forward'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.Spectrogram.html#modulation.torch_utilities.torch_transforms.Spectrogram.forward'>[docs]</a>    <span class='k'>def</span> <span class='nf'>forward</span><span class='p'>(</span>
+        <span class='bp'>self</span><span class='p'>,</span> <span class='n'>samples</span><span class='p'>:</span> <span class='n'>Union</span><span class='p'>[</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>],</span> <span class='n'>sample_rate</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span>
+    <span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>Tuple</span><span class='p'>[</span><span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>,</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>]:</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>        Apply transfrom</span>
+<span class='sd'>        :return: two tensors</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='k'>with</span> <span class='n'>warnings</span><span class='o'>.</span><span class='n'>catch_warnings</span><span class='p'>():</span>
+            <span class='n'>warnings</span><span class='o'>.</span><span class='n'>simplefilter</span><span class='p'>(</span><span class='s2'>&quot;ignore&quot;</span><span class='p'>)</span>
+            <span class='n'>spec</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>stft</span><span class='p'>(</span>
+                <span class='n'>samples</span><span class='p'>,</span>
+                <span class='n'>n_fft</span><span class='o'>=</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>n_fft</span><span class='p'>,</span>
+                <span class='n'>win_length</span><span class='o'>=</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>win_length</span><span class='p'>,</span>
+                <span class='n'>hop_length</span><span class='o'>=</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>hop_length</span><span class='p'>,</span>
+            <span class='p'>)</span>
+        <span class='n'>mag</span><span class='p'>,</span> <span class='n'>phase</span> <span class='o'>=</span> <span class='n'>torchaudio</span><span class='o'>.</span><span class='n'>functional</span><span class='o'>.</span><span class='n'>magphase</span><span class='p'>(</span><span class='n'>spec</span><span class='p'>)</span>
+        <span class='k'>return</span> <span class='n'>mag</span><span class='p'>,</span> <span class='n'>phase</span></div></div>
+
+
+<div class='viewcode-block' id='InverseSpectrogram'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.InverseSpectrogram.html#modulation.torch_utilities.torch_transforms.InverseSpectrogram'>[docs]</a><span class='k'>class</span> <span class='nc'>InverseSpectrogram</span><span class='p'>(</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Module</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    Convert from magphase to complex and perform istft</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+<div class='viewcode-block' id='InverseSpectrogram.__init__'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.InverseSpectrogram.html#modulation.torch_utilities.torch_transforms.InverseSpectrogram.__init__'>[docs]</a>    <span class='k'>def</span> <span class='fm'>__init__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>n_fft</span><span class='p'>,</span> <span class='n'>win_length</span><span class='p'>,</span> <span class='n'>hop_length</span><span class='p'>):</span>
+        <span class='nb'>super</span><span class='p'>()</span><span class='o'>.</span><span class='fm'>__init__</span><span class='p'>()</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>n_fft</span> <span class='o'>=</span> <span class='n'>n_fft</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>win_length</span> <span class='o'>=</span> <span class='n'>win_length</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>hop_length</span> <span class='o'>=</span> <span class='n'>hop_length</span></div>
+
+<div class='viewcode-block' id='InverseSpectrogram.forward'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.InverseSpectrogram.html#modulation.torch_utilities.torch_transforms.InverseSpectrogram.forward'>[docs]</a>    <span class='k'>def</span> <span class='nf'>forward</span><span class='p'>(</span>
+        <span class='bp'>self</span><span class='p'>,</span> <span class='n'>samples</span><span class='p'>:</span> <span class='n'>Union</span><span class='p'>[</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>],</span> <span class='n'>sample_rate</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span>
+    <span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>:</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>        :param samples:</span>
+<span class='sd'>        :type samples:</span>
+<span class='sd'>        :param sample_rate:</span>
+<span class='sd'>        :type sample_rate:</span>
+<span class='sd'>        :return:</span>
+<span class='sd'>        :rtype:</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='n'>mag</span><span class='p'>,</span> <span class='n'>phase</span> <span class='o'>=</span> <span class='n'>samples</span>
+        <span class='n'>spec</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>stack</span><span class='p'>([</span><span class='n'>torch</span><span class='o'>.</span><span class='n'>cos</span><span class='p'>(</span><span class='n'>phase</span><span class='p'>)</span> <span class='o'>*</span> <span class='n'>mag</span><span class='p'>,</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>sin</span><span class='p'>(</span><span class='n'>phase</span><span class='p'>)</span> <span class='o'>*</span> <span class='n'>mag</span><span class='p'>],</span> <span class='n'>dim</span><span class='o'>=-</span><span class='mi'>1</span><span class='p'>)</span>
+        <span class='k'>with</span> <span class='n'>warnings</span><span class='o'>.</span><span class='n'>catch_warnings</span><span class='p'>():</span>
+            <span class='n'>warnings</span><span class='o'>.</span><span class='n'>simplefilter</span><span class='p'>(</span><span class='s2'>&quot;ignore&quot;</span><span class='p'>)</span>
+            <span class='n'>signal</span> <span class='o'>=</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>istft</span><span class='p'>(</span>
+                <span class='n'>spec</span><span class='p'>,</span>
+                <span class='n'>n_fft</span><span class='o'>=</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>n_fft</span><span class='p'>,</span>
+                <span class='n'>win_length</span><span class='o'>=</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>win_length</span><span class='p'>,</span>
+                <span class='n'>hop_length</span><span class='o'>=</span><span class='bp'>self</span><span class='o'>.</span><span class='n'>hop_length</span><span class='p'>,</span>
+            <span class='p'>)</span>
+        <span class='k'>return</span> <span class='n'>signal</span></div></div>
+
+
+<div class='viewcode-block' id='ToMono'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.ToMono.html#modulation.torch_utilities.torch_transforms.ToMono'>[docs]</a><span class='k'>class</span> <span class='nc'>ToMono</span><span class='p'>(</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Module</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    Convert stereo signal to mono</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+    <span class='k'>def</span> <span class='fm'>__call__</span><span class='p'>(</span>
+        <span class='bp'>self</span><span class='p'>,</span> <span class='n'>samples</span><span class='p'>:</span> <span class='n'>Union</span><span class='p'>[</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>],</span> <span class='n'>sample_rate</span><span class='p'>:</span> <span class='nb'>int</span> <span class='o'>=</span> <span class='kc'>None</span>
+    <span class='p'>)</span> <span class='o'>-&gt;</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>:</span>
+        <span class='sd'>&quot;&quot;&quot;</span>
+
+<span class='sd'>        :param samples:</span>
+<span class='sd'>        :param sample_rate: dummy parameter for compatibility</span>
+<span class='sd'>        :return:</span>
+<span class='sd'>        &quot;&quot;&quot;</span>
+        <span class='k'>return</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>mean</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>,</span> <span class='n'>dim</span><span class='o'>=</span><span class='mi'>0</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='Squeeze'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.Squeeze.html#modulation.torch_utilities.torch_transforms.Squeeze'>[docs]</a><span class='k'>class</span> <span class='nc'>Squeeze</span><span class='p'>(</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Module</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    Transform to squeeze mono channel waveform</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+    <span class='k'>def</span> <span class='fm'>__call__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>samples</span><span class='p'>:</span> <span class='n'>Union</span><span class='p'>[</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>],</span> <span class='n'>sample_rate</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>):</span>
+        <span class='k'>return</span> <span class='n'>samples</span><span class='o'>.</span><span class='n'>squeeze</span><span class='p'>(</span><span class='mi'>0</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='ToNumpy'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.ToNumpy.html#modulation.torch_utilities.torch_transforms.ToNumpy'>[docs]</a><span class='k'>class</span> <span class='nc'>ToNumpy</span><span class='p'>(</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Module</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    Transform to make numpy array</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+    <span class='k'>def</span> <span class='fm'>__call__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>samples</span><span class='p'>:</span> <span class='n'>Union</span><span class='p'>[</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>],</span> <span class='n'>sample_rate</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>):</span>
+        <span class='k'>return</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>array</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='ToTorch'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.ToTorch.html#modulation.torch_utilities.torch_transforms.ToTorch'>[docs]</a><span class='k'>class</span> <span class='nc'>ToTorch</span><span class='p'>(</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Module</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    Transform to make torch.tensor</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+    <span class='k'>def</span> <span class='fm'>__call__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>samples</span><span class='p'>:</span> <span class='n'>Union</span><span class='p'>[</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>],</span> <span class='n'>sample_rate</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>):</span>
+        <span class='k'>return</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>tensor</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>)</span></div>
+
+
+<div class='viewcode-block' id='LogTransform'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.LogTransform.html#modulation.torch_utilities.torch_transforms.LogTransform'>[docs]</a><span class='k'>class</span> <span class='nc'>LogTransform</span><span class='p'>(</span><span class='n'>nn</span><span class='o'>.</span><span class='n'>Module</span><span class='p'>):</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
+<span class='sd'>    Transform for taking logarithm of mel spectrograms (or anything else)</span>
+<span class='sd'>    :param fill_value: value to substitute non-positive numbers with before applying log</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+
+<div class='viewcode-block' id='LogTransform.__init__'><a class='viewcode-back' href='../../../generated/modulation.torch_utilities.torch_transforms.LogTransform.html#modulation.torch_utilities.torch_transforms.LogTransform.__init__'>[docs]</a>    <span class='k'>def</span> <span class='fm'>__init__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>fill_value</span><span class='p'>:</span> <span class='nb'>float</span> <span class='o'>=</span> <span class='mf'>1e-5</span><span class='p'>)</span> <span class='o'>-&gt;</span> <span class='kc'>None</span><span class='p'>:</span>
+        <span class='nb'>super</span><span class='p'>()</span><span class='o'>.</span><span class='fm'>__init__</span><span class='p'>()</span>
+        <span class='bp'>self</span><span class='o'>.</span><span class='n'>fill_value</span> <span class='o'>=</span> <span class='n'>fill_value</span></div>
+
+    <span class='k'>def</span> <span class='fm'>__call__</span><span class='p'>(</span><span class='bp'>self</span><span class='p'>,</span> <span class='n'>samples</span><span class='p'>:</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>Tensor</span><span class='p'>,</span> <span class='n'>sample_rate</span><span class='p'>:</span> <span class='nb'>int</span><span class='p'>):</span>
+        <span class='n'>samples</span> <span class='o'>=</span> <span class='n'>samples</span><span class='o'>.</span><span class='n'>masked_fill</span><span class='p'>((</span><span class='n'>samples</span> <span class='o'>&lt;=</span> <span class='mi'>0</span><span class='p'>),</span> <span class='bp'>self</span><span class='o'>.</span><span class='n'>fill_value</span><span class='p'>)</span>
+        <span class='k'>return</span> <span class='n'>torch</span><span class='o'>.</span><span class='n'>log</span><span class='p'>(</span><span class='n'>samples</span><span class='p'>)</span></div>
+</pre>
+            </div>
 
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../getting_started.html'>Getting Started</a></li>
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+              <li><a href='../../../index.html'>Documentation overview</a>
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+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
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diff --git a/docs/github/html/_modules/modulation/visualisation/colorise_waveform.html b/docs/github/html/_modules/modulation/visualisation/colorise_waveform.html
index 1fd667d..483e8fc 100644
--- a/docs/github/html/_modules/modulation/visualisation/colorise_waveform.html
+++ b/docs/github/html/_modules/modulation/visualisation/colorise_waveform.html
@@ -1,173 +1,167 @@
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-<html lang="en">
+<html lang='en'>
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+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
     <title>modulation.visualisation.colorise_waveform &#8212; modulation 0.0.1 documentation</title>
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-  <h1>Source code for modulation.visualisation.colorise_waveform</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
-
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Christian&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
-
-<span class="s2">           Created on 29/03/2020</span>
-<span class="s2">           &quot;&quot;&quot;</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span>
-<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span>
-<span class="kn">from</span> <span class="nn">matplotlib.collections</span> <span class="kn">import</span> <span class="n">LineCollection</span>
-<span class="kn">from</span> <span class="nn">warg</span> <span class="kn">import</span> <span class="n">Number</span>
-
-
-<div class="viewcode-block" id="color_plot"><a class="viewcode-back" href="../../../generated/modulation.visualisation.colorise_waveform.color_plot.html#modulation.visualisation.colorise_waveform.color_plot">[docs]</a><span class="k">def</span> <span class="nf">color_plot</span><span class="p">(</span>
-    <span class="n">xy</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
-    <span class="n">colors</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Number</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">cmap</span><span class="o">=</span><span class="n">pyplot</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gist_rainbow</span><span class="p">,</span>  <span class="c1"># cmap=pyplot.cm.gist_ncar</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    :param xy:</span>
-<span class="sd">    :type xy:</span>
-<span class="sd">    :param colors:</span>
-<span class="sd">    :type colors:</span>
-<span class="sd">    :param cmap:</span>
-<span class="sd">    :type cmap:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-
-    <span class="c1"># Reshape things so that we have a sequence of:</span>
-    <span class="c1"># [[(x0,y0),(x1,y1)],[(x0,y0),(x1,y1)],...]</span>
-    <span class="n">xy</span> <span class="o">=</span> <span class="n">xy</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-    <span class="n">segments</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">xy</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">xy</span><span class="p">[</span><span class="mi">1</span><span class="p">:]])</span>
-
-    <span class="n">coll</span> <span class="o">=</span> <span class="n">LineCollection</span><span class="p">(</span><span class="n">segments</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">colors</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">colors</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">xy</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-    <span class="n">colors</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">xy</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="n">colors</span>
-    <span class="c1"># cyclic color = numpy.sin(colors)</span>
-    <span class="n">coll</span><span class="o">.</span><span class="n">set_array</span><span class="p">(</span><span class="n">colors</span><span class="p">)</span>
-
-    <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">coll</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">autoscale_view</span><span class="p">()</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-
-    <span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
-        <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-        <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">)</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-        <span class="n">color_plot</span><span class="p">(</span><span class="n">xy</span><span class="p">)</span>
-        <span class="n">pyplot</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-
-    <span class="n">main</span><span class="p">()</span>
-</pre></div>
+    <link href='../../../_static/pygments.css' rel='stylesheet' type='text/css'/>
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+
+            <h1>Source code for modulation.visualisation.colorise_waveform</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
+
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;Christian&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
+
+<span class='s2'>           Created on 29/03/2020</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
+
+<span class='kn'>from</span> <span class='nn'>typing</span> <span class='kn'>import</span> <span class='n'>Optional</span><span class='p'>,</span> <span class='n'>Sequence</span>
+
+<span class='kn'>import</span> <span class='nn'>numpy</span>
+<span class='kn'>from</span> <span class='nn'>matplotlib</span> <span class='kn'>import</span> <span class='n'>pyplot</span>
+<span class='kn'>from</span> <span class='nn'>matplotlib.collections</span> <span class='kn'>import</span> <span class='n'>LineCollection</span>
+<span class='kn'>from</span> <span class='nn'>warg</span> <span class='kn'>import</span> <span class='n'>Number</span>
 
 
+<div class='viewcode-block' id='color_plot'><a class='viewcode-back' href='../../../generated/modulation.visualisation.colorise_waveform.color_plot.html#modulation.visualisation.colorise_waveform.color_plot'>[docs]</a><span class='k'>def</span> <span class='nf'>color_plot</span><span class='p'>(</span>
+    <span class='n'>xy</span><span class='p'>:</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>ndarray</span><span class='p'>,</span>
+    <span class='n'>colors</span><span class='p'>:</span> <span class='n'>Optional</span><span class='p'>[</span><span class='n'>Sequence</span><span class='p'>[</span><span class='n'>Number</span><span class='p'>]]</span> <span class='o'>=</span> <span class='kc'>None</span><span class='p'>,</span>
+    <span class='n'>cmap</span><span class='o'>=</span><span class='n'>pyplot</span><span class='o'>.</span><span class='n'>cm</span><span class='o'>.</span><span class='n'>gist_rainbow</span><span class='p'>,</span>  <span class='c1'># cmap=pyplot.cm.gist_ncar</span>
+<span class='p'>)</span> <span class='o'>-&gt;</span> <span class='kc'>None</span><span class='p'>:</span>
+    <span class='sd'>&quot;&quot;&quot;</span>
 
+<span class='sd'>    :param xy:</span>
+<span class='sd'>    :type xy:</span>
+<span class='sd'>    :param colors:</span>
+<span class='sd'>    :type colors:</span>
+<span class='sd'>    :param cmap:</span>
+<span class='sd'>    :type cmap:</span>
+<span class='sd'>    &quot;&quot;&quot;</span>
+    <span class='n'>fig</span><span class='p'>,</span> <span class='n'>ax</span> <span class='o'>=</span> <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>subplots</span><span class='p'>()</span>
 
+    <span class='c1'># Reshape things so that we have a sequence of:</span>
+    <span class='c1'># [[(x0,y0),(x1,y1)],[(x0,y0),(x1,y1)],...]</span>
+    <span class='n'>xy</span> <span class='o'>=</span> <span class='n'>xy</span><span class='o'>.</span><span class='n'>reshape</span><span class='p'>(</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>,</span> <span class='mi'>1</span><span class='p'>,</span> <span class='mi'>2</span><span class='p'>)</span>
+    <span class='n'>segments</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>hstack</span><span class='p'>([</span><span class='n'>xy</span><span class='p'>[:</span><span class='o'>-</span><span class='mi'>1</span><span class='p'>],</span> <span class='n'>xy</span><span class='p'>[</span><span class='mi'>1</span><span class='p'>:]])</span>
 
+    <span class='n'>coll</span> <span class='o'>=</span> <span class='n'>LineCollection</span><span class='p'>(</span><span class='n'>segments</span><span class='p'>,</span> <span class='n'>cmap</span><span class='o'>=</span><span class='n'>cmap</span><span class='p'>)</span>
 
+    <span class='k'>if</span> <span class='n'>colors</span> <span class='ow'>is</span> <span class='kc'>None</span><span class='p'>:</span>
+        <span class='n'>colors</span> <span class='o'>=</span> <span class='n'>numpy</span><span class='o'>.</span><span class='n'>arange</span><span class='p'>(</span><span class='n'>xy</span><span class='o'>.</span><span class='n'>shape</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>])</span>
 
+    <span class='n'>colors</span> <span class='o'>=</span> <span class='p'>(</span><span class='mi'>1</span> <span class='o'>/</span> <span class='n'>xy</span><span class='o'>.</span><span class='n'>shape</span><span class='p'>[</span><span class='mi'>0</span><span class='p'>])</span> <span class='o'>*</span> <span class='n'>colors</span>
+    <span class='c1'># cyclic color = numpy.sin(colors)</span>
+    <span class='n'>coll</span><span class='o'>.</span><span class='n'>set_array</span><span class='p'>(</span><span class='n'>colors</span><span class='p'>)</span>
+
+    <span class='n'>ax</span><span class='o'>.</span><span class='n'>add_collection</span><span class='p'>(</span><span class='n'>coll</span><span class='p'>)</span>
+    <span class='n'>ax</span><span class='o'>.</span><span class='n'>autoscale_view</span><span class='p'>()</span></div>
+
+
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+
+    <span class='k'>def</span> <span class='nf'>main</span><span class='p'>():</span>
+        <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+        <span class='n'>xy</span> <span class='o'>=</span> <span class='p'>(</span><span class='n'>numpy</span><span class='o'>.</span><span class='n'>random</span><span class='o'>.</span><span class='n'>random</span><span class='p'>((</span><span class='mi'>1000</span><span class='p'>,</span> <span class='mi'>2</span><span class='p'>))</span> <span class='o'>-</span> <span class='mf'>0.5</span><span class='p'>)</span><span class='o'>.</span><span class='n'>cumsum</span><span class='p'>(</span><span class='n'>axis</span><span class='o'>=</span><span class='mi'>0</span><span class='p'>)</span>
+
+        <span class='n'>color_plot</span><span class='p'>(</span><span class='n'>xy</span><span class='p'>)</span>
+        <span class='n'>pyplot</span><span class='o'>.</span><span class='n'>show</span><span class='p'>()</span>
+
+    <span class='n'>main</span><span class='p'>()</span>
+</pre>
+            </div>
+
+          </div>
 
         </div>
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-      <div class="clearer"></div>
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../getting_started.html'>Getting Started</a></li>
+          </ul>
+
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diff --git a/docs/github/html/_modules/modulation/visualisation/salience/colored.html b/docs/github/html/_modules/modulation/visualisation/salience/colored.html
index c38ce95..f15743b 100644
--- a/docs/github/html/_modules/modulation/visualisation/salience/colored.html
+++ b/docs/github/html/_modules/modulation/visualisation/salience/colored.html
@@ -1,136 +1,130 @@
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-          <div class="body" role="main">
-            
-  <h1>Source code for modulation.visualisation.salience.colored</h1><div class="highlight"><pre>
-<span></span><span class="ch">#!/usr/bin/env python3</span>
-<span class="c1"># -*- coding: utf-8 -*-</span>
+    <meta content='width=device-width, initial-scale=0.9, maximum-scale=0.9' name='viewport'/>
 
-<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;heider&quot;</span>
-<span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
+  </head>
+  <body>
 
-<span class="s2">           Created on 8/2/22</span>
-<span class="s2">           &quot;&quot;&quot;</span>
 
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[]</span>
+    <div class='document'>
+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
 
 
-<div class="viewcode-block" id="colorise_waveform_frequency_by_components"><a class="viewcode-back" href="../../../../generated/modulation.visualisation.salience.colored.colorise_waveform_frequency_by_components.html#modulation.visualisation.salience.colored.colorise_waveform_frequency_by_components">[docs]</a><span class="k">def</span> <span class="nf">colorise_waveform_frequency_by_components</span><span class="p">():</span>
-    <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-    <span class="k">pass</span></div>
+          <div class='body' role='main'>
 
+            <h1>Source code for modulation.visualisation.salience.colored</h1>
+            <div class='highlight'><pre>
+<span></span><span class='ch'>#!/usr/bin/env python3</span>
+<span class='c1'># -*- coding: utf-8 -*-</span>
 
-<div class="viewcode-block" id="colorise_waveform_by_salience"><a class="viewcode-back" href="../../../../generated/modulation.visualisation.salience.colored.colorise_waveform_by_salience.html#modulation.visualisation.salience.colored.colorise_waveform_by_salience">[docs]</a><span class="k">def</span> <span class="nf">colorise_waveform_by_salience</span><span class="p">():</span>
-    <span class="sd">&quot;&quot;&quot;description&quot;&quot;&quot;</span>
-    <span class="k">pass</span></div>
+<span class='n'>__author__</span> <span class='o'>=</span> <span class='s2'>&quot;heider&quot;</span>
+<span class='vm'>__doc__</span> <span class='o'>=</span> <span class='sa'>r</span><span class='s2'>&quot;&quot;&quot;</span>
 
+<span class='s2'>           Created on 8/2/22</span>
+<span class='s2'>           &quot;&quot;&quot;</span>
 
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-    <span class="n">colorise_waveform_by_salience</span><span class="p">()</span>
-</pre></div>
+<span class='n'>__all__</span> <span class='o'>=</span> <span class='p'>[]</span>
 
-          </div>
-          
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-            <p class="logo"><a href="../../../../index.html">
-              <img class="logo" src="../../../../_static/mod.svg" alt="Logo"/>
-            </a></p>
-<h1 class="logo"><a href="../../../../index.html">modulation</a></h1>
-
-
-
-
-
-
-
-
-<h3>Navigation</h3>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../../generated/modulation.html">modulation</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notes</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../../getting_started.html">Getting Started</a></li>
-</ul>
-
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../../../../index.html">Documentation overview</a><ul>
-  <li><a href="../../../index.html">Module code</a><ul>
-  <li><a href="../../../modulation.html">modulation</a><ul>
-  </ul></li>
-  </ul></li>
-  </ul></li>
-</ul>
-</div>
-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../../../../search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
-      <input type="submit" value="Go" />
-    </form>
-    </div>
-</div>
-<script>document.getElementById('searchbox').style.display = "block"</script>
 
+<div class='viewcode-block' id='colorise_waveform_frequency_by_components'><a class='viewcode-back' href='../../../../generated/modulation.visualisation.salience.colored.colorise_waveform_frequency_by_components.html#modulation.visualisation.salience.colored.colorise_waveform_frequency_by_components'>[docs]</a><span class='k'>def</span> <span class='nf'>colorise_waveform_frequency_by_components</span><span class='p'>():</span>
+    <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+    <span class='k'>pass</span></div>
 
 
+<div class='viewcode-block' id='colorise_waveform_by_salience'><a class='viewcode-back' href='../../../../generated/modulation.visualisation.salience.colored.colorise_waveform_by_salience.html#modulation.visualisation.salience.colored.colorise_waveform_by_salience'>[docs]</a><span class='k'>def</span> <span class='nf'>colorise_waveform_by_salience</span><span class='p'>():</span>
+    <span class='sd'>&quot;&quot;&quot;description&quot;&quot;&quot;</span>
+    <span class='k'>pass</span></div>
 
 
+<span class='k'>if</span> <span class='vm'>__name__</span> <span class='o'>==</span> <span class='s2'>&quot;__main__&quot;</span><span class='p'>:</span>
+    <span class='n'>colorise_waveform_by_salience</span><span class='p'>()</span>
+</pre>
+            </div>
 
+          </div>
+
+        </div>
+      </div>
+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <p class='logo'><a href='../../../../index.html'>
+            <img alt='Logo' class='logo' src='../../../../_static/mod.svg'/>
+          </a></p>
+          <h1 class='logo'><a href='../../../../index.html'>modulation</a></h1>
+
+
+          <h3>Navigation</h3>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../../generated/modulation.html'>modulation</a></li>
+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../../../../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../../../../index.html'>Documentation overview</a>
+                <ul>
+                  <li><a href='../../../index.html'>Module code</a>
+                    <ul>
+                      <li><a href='../../../modulation.html'>modulation</a>
+                        <ul>
+                        </ul>
+                      </li>
+                    </ul>
+                  </li>
+                </ul>
+              </li>
+            </ul>
+          </div>
+          <div id='searchbox' role='search' style='display: none'>
+            <h3 id='searchlabel'>Quick search</h3>
+            <div class='searchformwrapper'>
+              <form action='../../../../search.html' class='search' method='get'>
+                <input aria-labelledby='searchlabel' autocapitalize='off' autocomplete='off' autocorrect='off' name='q' spellcheck='false' type='text'/>
+                <input type='submit' value='Go'/>
+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
 
 
         </div>
       </div>
-      <div class="clearer"></div>
+      <div class='clearer'></div>
     </div>
-    <div class="footer">
+    <div class='footer'>
       &copy;.
-      
+
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
-      
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
+
     </div>
 
-    
 
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.de_emphasise.rst.txt b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.de_emphasise.rst.txt
new file mode 100644
index 0000000..1efde08
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.de_emphasise.rst.txt
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.funcs.de\_emphasise
+===============================================
+
+.. currentmodule:: modulation.audio_utilities.funcs
+
+.. autofunction:: de_emphasise
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.fft_frequencies.rst.txt b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.fft_frequencies.rst.txt
new file mode 100644
index 0000000..49e88ec
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.fft_frequencies.rst.txt
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.funcs.fft\_frequencies
+==================================================
+
+.. currentmodule:: modulation.audio_utilities.funcs
+
+.. autofunction:: fft_frequencies
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.hilbert_envelope.rst.txt b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.hilbert_envelope.rst.txt
new file mode 100644
index 0000000..691c557
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.hilbert_envelope.rst.txt
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.funcs.hilbert\_envelope
+===================================================
+
+.. currentmodule:: modulation.audio_utilities.funcs
+
+.. autofunction:: hilbert_envelope
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.in_mel_scale.rst.txt b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.in_mel_scale.rst.txt
new file mode 100644
index 0000000..5077e1a
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.in_mel_scale.rst.txt
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.funcs.in\_mel\_scale
+================================================
+
+.. currentmodule:: modulation.audio_utilities.funcs
+
+.. autofunction:: in_mel_scale
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.mel_scale.rst.txt b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.mel_scale.rst.txt
new file mode 100644
index 0000000..d98fade
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.mel_scale.rst.txt
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.funcs.mel\_scale
+============================================
+
+.. currentmodule:: modulation.audio_utilities.funcs
+
+.. autofunction:: mel_scale
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.pre_emphasise.rst.txt b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.pre_emphasise.rst.txt
new file mode 100644
index 0000000..66bf045
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.pre_emphasise.rst.txt
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.funcs.pre\_emphasise
+================================================
+
+.. currentmodule:: modulation.audio_utilities.funcs
+
+.. autofunction:: pre_emphasise
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.pre_emphasise_torch.rst.txt b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.pre_emphasise_torch.rst.txt
new file mode 100644
index 0000000..f555f06
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.pre_emphasise_torch.rst.txt
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.funcs.pre\_emphasise\_torch
+=======================================================
+
+.. currentmodule:: modulation.audio_utilities.funcs
+
+.. autofunction:: pre_emphasise_torch
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.rst.txt b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.rst.txt
new file mode 100644
index 0000000..a0b51f5
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.audio_utilities.funcs.rst.txt
@@ -0,0 +1,36 @@
+modulation.audio\_utilities.funcs
+=================================
+
+.. automodule:: modulation.audio_utilities.funcs
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      de_emphasise
+      fft_frequencies
+      hilbert_envelope
+      in_mel_scale
+      mel_scale
+      pre_emphasise
+      pre_emphasise_torch
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/github/html/_sources/generated/modulation.audio_utilities.rst.txt b/docs/github/html/_sources/generated/modulation.audio_utilities.rst.txt
new file mode 100644
index 0000000..8acf104
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.audio_utilities.rst.txt
@@ -0,0 +1,35 @@
+modulation.audio\_utilities
+===========================
+
+.. automodule:: modulation.audio_utilities
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom_autosummary/module.rst
+   :recursive:
+
+   modulation.audio_utilities.funcs
+   modulation.audio_utilities.persistence
+   modulation.audio_utilities.splitting
+   modulation.audio_utilities.torch_transforms
+
diff --git a/docs/github/html/_sources/generated/modulation.audio_utilities.splitting.mask_split_speech_silence.rst.txt b/docs/github/html/_sources/generated/modulation.audio_utilities.splitting.mask_split_speech_silence.rst.txt
new file mode 100644
index 0000000..dd7bd5d
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.audio_utilities.splitting.mask_split_speech_silence.rst.txt
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.splitting.mask\_split\_speech\_silence
+==================================================================
+
+.. currentmodule:: modulation.audio_utilities.splitting
+
+.. autofunction:: mask_split_speech_silence
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.audio_utilities.splitting.rst.txt b/docs/github/html/_sources/generated/modulation.audio_utilities.splitting.rst.txt
new file mode 100644
index 0000000..9d2b515
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.audio_utilities.splitting.rst.txt
@@ -0,0 +1,30 @@
+modulation.audio\_utilities.splitting
+=====================================
+
+.. automodule:: modulation.audio_utilities.splitting
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      mask_split_speech_silence
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/github/html/_sources/generated/modulation.classification.metrics.lwlrap.calculate_overall_lwlrap_sklearn.rst.txt b/docs/github/html/_sources/generated/modulation.classification.metrics.lwlrap.calculate_overall_lwlrap_sklearn.rst.txt
new file mode 100644
index 0000000..53d6961
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.classification.metrics.lwlrap.calculate_overall_lwlrap_sklearn.rst.txt
@@ -0,0 +1,6 @@
+modulation.classification.metrics.lwlrap.calculate\_overall\_lwlrap\_sklearn
+============================================================================
+
+.. currentmodule:: modulation.classification.metrics.lwlrap
+
+.. autofunction:: calculate_overall_lwlrap_sklearn
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.classification.procs.rst.txt b/docs/github/html/_sources/generated/modulation.classification.procs.rst.txt
new file mode 100644
index 0000000..77eb33b
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.classification.procs.rst.txt
@@ -0,0 +1,31 @@
+modulation.classification.procs
+===============================
+
+.. automodule:: modulation.classification.procs
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      single_epoch_evaluation
+      single_epoch_fitting
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/github/html/_sources/generated/modulation.classification.procs.single_epoch_evaluation.rst.txt b/docs/github/html/_sources/generated/modulation.classification.procs.single_epoch_evaluation.rst.txt
new file mode 100644
index 0000000..bc9497a
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.classification.procs.single_epoch_evaluation.rst.txt
@@ -0,0 +1,6 @@
+modulation.classification.procs.single\_epoch\_evaluation
+=========================================================
+
+.. currentmodule:: modulation.classification.procs
+
+.. autofunction:: single_epoch_evaluation
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.classification.procs.single_epoch_fitting.rst.txt b/docs/github/html/_sources/generated/modulation.classification.procs.single_epoch_fitting.rst.txt
new file mode 100644
index 0000000..924ad56
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.classification.procs.single_epoch_fitting.rst.txt
@@ -0,0 +1,6 @@
+modulation.classification.procs.single\_epoch\_fitting
+======================================================
+
+.. currentmodule:: modulation.classification.procs
+
+.. autofunction:: single_epoch_fitting
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.denoising.band_pass.rst.txt b/docs/github/html/_sources/generated/modulation.denoising.band_pass.rst.txt
new file mode 100644
index 0000000..f864a05
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.denoising.band_pass.rst.txt
@@ -0,0 +1,23 @@
+modulation.denoising.band\_pass
+===============================
+
+.. automodule:: modulation.denoising.band_pass
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/github/html/_sources/generated/modulation.denoising.finger_print.rst.txt b/docs/github/html/_sources/generated/modulation.denoising.finger_print.rst.txt
new file mode 100644
index 0000000..28ec652
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.denoising.finger_print.rst.txt
@@ -0,0 +1,23 @@
+modulation.denoising.finger\_print
+==================================
+
+.. automodule:: modulation.denoising.finger_print
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/github/html/_sources/generated/modulation.denoising.rst.txt b/docs/github/html/_sources/generated/modulation.denoising.rst.txt
new file mode 100644
index 0000000..31b62ca
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.denoising.rst.txt
@@ -0,0 +1,33 @@
+modulation.denoising
+====================
+
+.. automodule:: modulation.denoising
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom_autosummary/module.rst
+   :recursive:
+
+   modulation.denoising.band_pass
+   modulation.denoising.finger_print
+
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.additive_noise.compute_additive_noise_samples.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.additive_noise.compute_additive_noise_samples.rst.txt
new file mode 100644
index 0000000..b46d301
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.additive_noise.compute_additive_noise_samples.rst.txt
@@ -0,0 +1,6 @@
+modulation.noise\_generation.additive\_noise.compute\_additive\_noise\_samples
+==============================================================================
+
+.. currentmodule:: modulation.noise_generation.additive_noise
+
+.. autofunction:: compute_additive_noise_samples
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.additive_noise.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.additive_noise.rst.txt
new file mode 100644
index 0000000..79fdefe
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.additive_noise.rst.txt
@@ -0,0 +1,31 @@
+modulation.noise\_generation.additive\_noise
+============================================
+
+.. automodule:: modulation.noise_generation.additive_noise
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      compute_additive_noise_samples
+      sample_noise
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.additive_noise.sample_noise.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.additive_noise.sample_noise.rst.txt
new file mode 100644
index 0000000..747a3e5
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.additive_noise.sample_noise.rst.txt
@@ -0,0 +1,6 @@
+modulation.noise\_generation.additive\_noise.sample\_noise
+==========================================================
+
+.. currentmodule:: modulation.noise_generation.additive_noise
+
+.. autofunction:: sample_noise
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.babble_noise.generate_babble_noise.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.babble_noise.generate_babble_noise.rst.txt
new file mode 100644
index 0000000..8593850
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.babble_noise.generate_babble_noise.rst.txt
@@ -0,0 +1,6 @@
+modulation.noise\_generation.babble\_noise.generate\_babble\_noise
+==================================================================
+
+.. currentmodule:: modulation.noise_generation.babble_noise
+
+.. autofunction:: generate_babble_noise
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.babble_noise.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.babble_noise.rst.txt
new file mode 100644
index 0000000..f3e5d69
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.babble_noise.rst.txt
@@ -0,0 +1,30 @@
+modulation.noise\_generation.babble\_noise
+==========================================
+
+.. automodule:: modulation.noise_generation.babble_noise
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      generate_babble_noise
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.deprecated.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.deprecated.rst.txt
new file mode 100644
index 0000000..9e88733
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.deprecated.rst.txt
@@ -0,0 +1,32 @@
+modulation.noise\_generation.deprecated
+=======================================
+
+.. automodule:: modulation.noise_generation.deprecated
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom_autosummary/module.rst
+   :recursive:
+
+   modulation.noise_generation.deprecated.additive_noise
+
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.rst.txt
new file mode 100644
index 0000000..a9abc53
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.rst.txt
@@ -0,0 +1,29 @@
+modulation.noise\_generation.gaussian\_noise.GaussianNoiseTypeEnum
+==================================================================
+
+.. currentmodule:: modulation.noise_generation.gaussian_noise
+
+.. autoclass:: GaussianNoiseTypeEnum
+   :members:
+   :show-inheritance:
+   :inherited-members:
+
+   
+   .. automethod:: __init__
+
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~GaussianNoiseTypeEnum.white
+      ~GaussianNoiseTypeEnum.brown
+      ~GaussianNoiseTypeEnum.blue
+      ~GaussianNoiseTypeEnum.pink
+      ~GaussianNoiseTypeEnum.violet
+   
+   
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.blue_noise.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.blue_noise.rst.txt
new file mode 100644
index 0000000..4588bf6
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.blue_noise.rst.txt
@@ -0,0 +1,6 @@
+modulation.noise\_generation.gaussian\_noise.blue\_noise
+========================================================
+
+.. currentmodule:: modulation.noise_generation.gaussian_noise
+
+.. autofunction:: blue_noise
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.brown_noise.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.brown_noise.rst.txt
new file mode 100644
index 0000000..d00a955
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.brown_noise.rst.txt
@@ -0,0 +1,6 @@
+modulation.noise\_generation.gaussian\_noise.brown\_noise
+=========================================================
+
+.. currentmodule:: modulation.noise_generation.gaussian_noise
+
+.. autofunction:: brown_noise
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.generate_noise.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.generate_noise.rst.txt
new file mode 100644
index 0000000..ac42c63
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.generate_noise.rst.txt
@@ -0,0 +1,6 @@
+modulation.noise\_generation.gaussian\_noise.generate\_noise
+============================================================
+
+.. currentmodule:: modulation.noise_generation.gaussian_noise
+
+.. autofunction:: generate_noise
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.pink_noise.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.pink_noise.rst.txt
new file mode 100644
index 0000000..c4ef50a
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.pink_noise.rst.txt
@@ -0,0 +1,6 @@
+modulation.noise\_generation.gaussian\_noise.pink\_noise
+========================================================
+
+.. currentmodule:: modulation.noise_generation.gaussian_noise
+
+.. autofunction:: pink_noise
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.rst.txt
new file mode 100644
index 0000000..7727e06
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.rst.txt
@@ -0,0 +1,43 @@
+modulation.noise\_generation.gaussian\_noise
+============================================
+
+.. automodule:: modulation.noise_generation.gaussian_noise
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      blue_noise
+      brown_noise
+      generate_noise
+      pink_noise
+      violet_noise
+      white_noise
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom_autosummary/class.rst
+   
+      GaussianNoiseTypeEnum
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.violet_noise.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.violet_noise.rst.txt
new file mode 100644
index 0000000..521da38
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.violet_noise.rst.txt
@@ -0,0 +1,6 @@
+modulation.noise\_generation.gaussian\_noise.violet\_noise
+==========================================================
+
+.. currentmodule:: modulation.noise_generation.gaussian_noise
+
+.. autofunction:: violet_noise
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.white_noise.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.white_noise.rst.txt
new file mode 100644
index 0000000..ebe8cd7
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.gaussian_noise.white_noise.rst.txt
@@ -0,0 +1,6 @@
+modulation.noise\_generation.gaussian\_noise.white\_noise
+=========================================================
+
+.. currentmodule:: modulation.noise_generation.gaussian_noise
+
+.. autofunction:: white_noise
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.rst.txt
new file mode 100644
index 0000000..4662bc4
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.rst.txt
@@ -0,0 +1,36 @@
+modulation.noise\_generation
+============================
+
+.. automodule:: modulation.noise_generation
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom_autosummary/module.rst
+   :recursive:
+
+   modulation.noise_generation.additive_noise
+   modulation.noise_generation.babble_noise
+   modulation.noise_generation.deprecated
+   modulation.noise_generation.gaussian_noise
+   modulation.noise_generation.shaped_noise
+
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.shaped_noise.generate_speech_shaped_noise.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.shaped_noise.generate_speech_shaped_noise.rst.txt
new file mode 100644
index 0000000..2c5f635
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.shaped_noise.generate_speech_shaped_noise.rst.txt
@@ -0,0 +1,6 @@
+modulation.noise\_generation.shaped\_noise.generate\_speech\_shaped\_noise
+==========================================================================
+
+.. currentmodule:: modulation.noise_generation.shaped_noise
+
+.. autofunction:: generate_speech_shaped_noise
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.noise_generation.shaped_noise.rst.txt b/docs/github/html/_sources/generated/modulation.noise_generation.shaped_noise.rst.txt
new file mode 100644
index 0000000..8ba3b87
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.noise_generation.shaped_noise.rst.txt
@@ -0,0 +1,30 @@
+modulation.noise\_generation.shaped\_noise
+==========================================
+
+.. automodule:: modulation.noise_generation.shaped_noise
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      generate_speech_shaped_noise
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/github/html/_sources/generated/modulation.salience.colorise_waveform.color_plot.rst.txt b/docs/github/html/_sources/generated/modulation.salience.colorise_waveform.color_plot.rst.txt
new file mode 100644
index 0000000..c1238ee
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.salience.colorise_waveform.color_plot.rst.txt
@@ -0,0 +1,6 @@
+modulation.salience.colorise\_waveform.color\_plot
+==================================================
+
+.. currentmodule:: modulation.salience.colorise_waveform
+
+.. autofunction:: color_plot
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.salience.colorise_waveform.colorise_waveform_by_salience.rst.txt b/docs/github/html/_sources/generated/modulation.salience.colorise_waveform.colorise_waveform_by_salience.rst.txt
new file mode 100644
index 0000000..e42e198
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.salience.colorise_waveform.colorise_waveform_by_salience.rst.txt
@@ -0,0 +1,6 @@
+modulation.salience.colorise\_waveform.colorise\_waveform\_by\_salience
+=======================================================================
+
+.. currentmodule:: modulation.salience.colorise_waveform
+
+.. autofunction:: colorise_waveform_by_salience
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components.rst.txt b/docs/github/html/_sources/generated/modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components.rst.txt
new file mode 100644
index 0000000..1518eb4
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components.rst.txt
@@ -0,0 +1,6 @@
+modulation.salience.colorise\_waveform.colorise\_waveform\_frequency\_by\_components
+====================================================================================
+
+.. currentmodule:: modulation.salience.colorise_waveform
+
+.. autofunction:: colorise_waveform_frequency_by_components
\ No newline at end of file
diff --git a/docs/github/html/_sources/generated/modulation.salience.colorise_waveform.rst.txt b/docs/github/html/_sources/generated/modulation.salience.colorise_waveform.rst.txt
new file mode 100644
index 0000000..cbcb661
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.salience.colorise_waveform.rst.txt
@@ -0,0 +1,32 @@
+modulation.salience.colorise\_waveform
+======================================
+
+.. automodule:: modulation.salience.colorise_waveform
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      color_plot
+      colorise_waveform_by_salience
+      colorise_waveform_frequency_by_components
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/github/html/_sources/generated/modulation.salience.rst.txt b/docs/github/html/_sources/generated/modulation.salience.rst.txt
new file mode 100644
index 0000000..5a1ae59
--- /dev/null
+++ b/docs/github/html/_sources/generated/modulation.salience.rst.txt
@@ -0,0 +1,32 @@
+modulation.salience
+===================
+
+.. automodule:: modulation.salience
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom_autosummary/module.rst
+   :recursive:
+
+   modulation.salience.colorise_waveform
+
diff --git a/docs/github/html/_sources/index.rst.txt b/docs/github/html/_sources/index.rst.txt
index 00e41f0..3463399 100644
--- a/docs/github/html/_sources/index.rst.txt
+++ b/docs/github/html/_sources/index.rst.txt
@@ -1,4 +1,4 @@
-|organisation| |project| Documentation
+|organisation| |project|Documentation
 ===============================
 
 Version: |version| |today|
diff --git a/docs/github/html/_static/matplotlib.svg b/docs/github/html/_static/matplotlib.svg
index b6c2c6b..a5aae94 100644
--- a/docs/github/html/_static/matplotlib.svg
+++ b/docs/github/html/_static/matplotlib.svg
@@ -1 +1,202 @@
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diff --git a/docs/github/html/generated/modulation.audio_utilities.funcs.de_emphasise.html b/docs/github/html/generated/modulation.audio_utilities.funcs.de_emphasise.html
new file mode 100644
index 0000000..e98129e
--- /dev/null
+++ b/docs/github/html/generated/modulation.audio_utilities.funcs.de_emphasise.html
@@ -0,0 +1,123 @@
+<!DOCTYPE html>
+
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+
+    <title>modulation.audio_utilities.funcs.de_emphasise &#8212; modulation 0.0.1 documentation</title>
+    <link rel='stylesheet' type='text/css' href='../_static/pygments.css'/>
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+        <div class='bodywrapper'>
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+          <div class='body' role='main'>
+
+            <section id='modulation-audio-utilities-funcs-de-emphasise'>
+              <h1>modulation.audio_utilities.funcs.de_emphasise<a class='headerlink' href='#modulation-audio-utilities-funcs-de-emphasise' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.audio_utilities.funcs.de_emphasise'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.audio_utilities.funcs.</span></span><span class='sig-name descname'><span class='pre'>de_emphasise</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>x</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>coeff</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#float' title='(in Python v3.10)'><span class='pre'>float</span></a></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>0.97</span></span></em><span class='sig-paren'>)</span><a class='reference internal' href='../_modules/modulation/audio_utilities/funcs.html#de_emphasise'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a
+                    class='headerlink' href='#modulation.audio_utilities.funcs.de_emphasise' title='Permalink to this definition'>¶</a></dt>
+                <dd>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'>
+                      <ul class='simple'>
+                        <li><p><strong>x</strong> – </p></li>
+                        <li><p><strong>coeff</strong> – </p></li>
+                      </ul>
+                    </dd>
+                    <dt class='field-even'>Returns</dt>
+                    <dd class='field-even'><p></p>
+                    </dd>
+                    <dt class='field-odd'>Return type</dt>
+                    <dd class='field-odd'><p></p>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
+            </section>
+
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+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
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+</html>
\ No newline at end of file
diff --git a/docs/github/html/generated/modulation.audio_utilities.funcs.fft_frequencies.html b/docs/github/html/generated/modulation.audio_utilities.funcs.fft_frequencies.html
new file mode 100644
index 0000000..c88c1ed
--- /dev/null
+++ b/docs/github/html/generated/modulation.audio_utilities.funcs.fft_frequencies.html
@@ -0,0 +1,122 @@
+<!DOCTYPE html>
+
+<html lang='en'>
+  <head>
+    <meta charset='utf-8'/>
+    <meta name='viewport' content='width=device-width, initial-scale=1.0'/>
+    <meta name='generator' content='Docutils 0.17.1: http://docutils.sourceforge.net/'/>
+
+    <title>modulation.audio_utilities.funcs.fft_frequencies &#8212; modulation 0.0.1 documentation</title>
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+
+            <section id='modulation-audio-utilities-funcs-fft-frequencies'>
+              <h1>modulation.audio_utilities.funcs.fft_frequencies<a class='headerlink' href='#modulation-audio-utilities-funcs-fft-frequencies' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.audio_utilities.funcs.fft_frequencies'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.audio_utilities.funcs.</span></span><span class='sig-name descname'><span class='pre'>fft_frequencies</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>sr</span></span><span class='o'><span class='pre'>=</span></span><span class='default_value'><span class='pre'>16000</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>n_fft</span></span><span class='o'><span class='pre'>=</span></span><span class='default_value'><span class='pre'>512</span></span></em><span class='sig-paren'>)</span><a class='reference internal' href='../_modules/modulation/audio_utilities/funcs.html#fft_frequencies'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.audio_utilities.funcs.fft_frequencies' title='Permalink to this definition'>¶</a></dt>
+                <dd>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'>
+                      <ul class='simple'>
+                        <li><p><strong>sr</strong> – </p></li>
+                        <li><p><strong>n_fft</strong> – </p></li>
+                      </ul>
+                    </dd>
+                    <dt class='field-even'>Returns</dt>
+                    <dd class='field-even'><p></p>
+                    </dd>
+                    <dt class='field-odd'>Return type</dt>
+                    <dd class='field-odd'><p></p>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
+            </section>
+
+
+          </div>
+
+        </div>
+      </div>
+      <div class='sphinxsidebar' role='navigation' aria-label='main navigation'>
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
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+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../index.html'>Documentation overview</a>
+                <ul>
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+
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+         rel='nofollow'>Page source</a>
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+</html>
\ No newline at end of file
diff --git a/docs/github/html/generated/modulation.audio_utilities.funcs.hilbert_envelope.html b/docs/github/html/generated/modulation.audio_utilities.funcs.hilbert_envelope.html
new file mode 100644
index 0000000..1fb3034
--- /dev/null
+++ b/docs/github/html/generated/modulation.audio_utilities.funcs.hilbert_envelope.html
@@ -0,0 +1,120 @@
+<!DOCTYPE html>
+
+<html lang='en'>
+  <head>
+    <meta charset='utf-8'/>
+    <meta name='viewport' content='width=device-width, initial-scale=1.0'/>
+    <meta name='generator' content='Docutils 0.17.1: http://docutils.sourceforge.net/'/>
+
+    <title>modulation.audio_utilities.funcs.hilbert_envelope &#8212; modulation 0.0.1 documentation</title>
+    <link rel='stylesheet' type='text/css' href='../_static/pygments.css'/>
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+    <link rel='canonical' href='aivclab.github.io/modulation/generated/modulation.audio_utilities.funcs.hilbert_envelope.html'/>
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+
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+
+            <section id='modulation-audio-utilities-funcs-hilbert-envelope'>
+              <h1>modulation.audio_utilities.funcs.hilbert_envelope<a class='headerlink' href='#modulation-audio-utilities-funcs-hilbert-envelope' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.audio_utilities.funcs.hilbert_envelope'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.audio_utilities.funcs.</span></span><span class='sig-name descname'><span class='pre'>hilbert_envelope</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>signal</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray' title='(in NumPy v1.23)'><span class='pre'>ndarray</span></a></span></em>, <em class='sig-param'><span class='o'><span class='pre'>*</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>n_fft</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Optional' title='(in Python v3.10)'><span
+                    class='pre'>Optional</span></a><span class='p'><span class='pre'>[</span></span><a class='reference external' href='https://docs.python.org/3/library/functions.html#int' title='(in Python v3.10)'><span class='pre'>int</span></a><span class='p'><span class='pre'>]</span></span></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>None</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray' title='(in NumPy v1.23)'><span class='pre'>ndarray</span></a></span></span><a class='reference internal' href='../_modules/modulation/audio_utilities/funcs.html#hilbert_envelope'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     href='#modulation.audio_utilities.funcs.hilbert_envelope'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     title='Permalink to this definition'>¶</a>
+                </dt>
+                <dd><p>Calculates the Hilbert envelope of a signal.</p>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'><p><strong>signal</strong> (<em>array_like</em>) – Signal on which to calculate the hilbert envelope. The calculation
+                                                                     is done along the last axis (i.e. <code class='docutils literal notranslate'><span class='pre'>axis=-1</span></code>).</p>
+                    </dd>
+                    <dt class='field-even'>Return type</dt>
+                    <dd class='field-even'><p>ndarray</p>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
+            </section>
+
+
+          </div>
+
+        </div>
+      </div>
+      <div class='sphinxsidebar' role='navigation' aria-label='main navigation'>
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+            <li class='toctree-l1'><a class='reference internal' href='modulation.html'>modulation</a></li>
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../index.html'>Documentation overview</a>
+                <ul>
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+
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+         rel='nofollow'>Page source</a>
+    </div>
+
+
+  </body>
+</html>
\ No newline at end of file
diff --git a/docs/github/html/generated/modulation.audio_utilities.funcs.html b/docs/github/html/generated/modulation.audio_utilities.funcs.html
new file mode 100644
index 0000000..b089122
--- /dev/null
+++ b/docs/github/html/generated/modulation.audio_utilities.funcs.html
@@ -0,0 +1,164 @@
+<!DOCTYPE html>
+
+<html lang='en'>
+  <head>
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
+    <meta content='Docutils 0.17.1: http://docutils.sourceforge.net/' name='generator'/>
+
+    <title>modulation.audio_utilities.funcs &#8212; modulation 0.0.1 documentation</title>
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+    <link href='../genindex.html' rel='index' title='Index'/>
+    <link href='../search.html' rel='search' title='Search'/>
+
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+
+            <section id='module-modulation.audio_utilities.funcs'>
+              <span id='modulation-audio-utilities-funcs'></span>
+              <h1>modulation.audio_utilities.funcs<a class='headerlink' href='#module-modulation.audio_utilities.funcs' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 11-01-2021</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.audio_utilities.funcs.de_emphasise.html#modulation.audio_utilities.funcs.de_emphasise' title='modulation.audio_utilities.funcs.de_emphasise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>de_emphasise</span></code></a>(x[, coeff])</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param x</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.audio_utilities.funcs.fft_frequencies.html#modulation.audio_utilities.funcs.fft_frequencies' title='modulation.audio_utilities.funcs.fft_frequencies'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>fft_frequencies</span></code></a>([sr, n_fft])</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param sr</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.audio_utilities.funcs.hilbert_envelope.html#modulation.audio_utilities.funcs.hilbert_envelope' title='modulation.audio_utilities.funcs.hilbert_envelope'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>hilbert_envelope</span></code></a>(signal, *[, n_fft])</p></td>
+                    <td><p>Calculates the Hilbert envelope of a signal.</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.audio_utilities.funcs.in_mel_scale.html#modulation.audio_utilities.funcs.in_mel_scale' title='modulation.audio_utilities.funcs.in_mel_scale'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>in_mel_scale</span></code></a>(x)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param x</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.audio_utilities.funcs.mel_scale.html#modulation.audio_utilities.funcs.mel_scale' title='modulation.audio_utilities.funcs.mel_scale'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>mel_scale</span></code></a>(x)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param x</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.audio_utilities.funcs.pre_emphasise.html#modulation.audio_utilities.funcs.pre_emphasise' title='modulation.audio_utilities.funcs.pre_emphasise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>pre_emphasise</span></code></a>(x[, coeff])</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param x</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.audio_utilities.funcs.pre_emphasise_torch.html#modulation.audio_utilities.funcs.pre_emphasise_torch' title='modulation.audio_utilities.funcs.pre_emphasise_torch'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>pre_emphasise_torch</span></code></a>(signal[, preemph])</p></td>
+                    <td><p>Pre-emphasis on the input signal :param signal: (time,) :param preemph: :return: (time,)</p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
+
+
+          </div>
+
+        </div>
+      </div>
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../index.html'>Documentation overview</a>
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+         rel='nofollow'>Page source</a>
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\ No newline at end of file
diff --git a/docs/github/html/generated/modulation.audio_utilities.funcs.in_mel_scale.html b/docs/github/html/generated/modulation.audio_utilities.funcs.in_mel_scale.html
new file mode 100644
index 0000000..968a4aa
--- /dev/null
+++ b/docs/github/html/generated/modulation.audio_utilities.funcs.in_mel_scale.html
@@ -0,0 +1,118 @@
+<!DOCTYPE html>
+
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+
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+
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+
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+
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+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
+
+
+          <div class='body' role='main'>
+
+            <section id='modulation-audio-utilities-funcs-in-mel-scale'>
+              <h1>modulation.audio_utilities.funcs.in_mel_scale<a class='headerlink' href='#modulation-audio-utilities-funcs-in-mel-scale' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.audio_utilities.funcs.in_mel_scale'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.audio_utilities.funcs.</span></span><span class='sig-name descname'><span class='pre'>in_mel_scale</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>x</span></span></em><span class='sig-paren'>)</span><a class='reference internal' href='../_modules/modulation/audio_utilities/funcs.html#in_mel_scale'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.audio_utilities.funcs.in_mel_scale' title='Permalink to this definition'>¶</a></dt>
+                <dd>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'><p><strong>x</strong> – </p>
+                    </dd>
+                    <dt class='field-even'>Returns</dt>
+                    <dd class='field-even'><p></p>
+                    </dd>
+                    <dt class='field-odd'>Return type</dt>
+                    <dd class='field-odd'><p></p>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
+            </section>
+
+
+          </div>
+
+        </div>
+      </div>
+      <div class='sphinxsidebar' role='navigation' aria-label='main navigation'>
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
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+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../index.html'>Documentation overview</a>
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+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
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+
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+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
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+
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+      <a href='../_sources/generated/modulation.audio_utilities.funcs.in_mel_scale.rst.txt'
+         rel='nofollow'>Page source</a>
+    </div>
+
+
+  </body>
+</html>
\ No newline at end of file
diff --git a/docs/github/html/generated/modulation.audio_utilities.funcs.mel_scale.html b/docs/github/html/generated/modulation.audio_utilities.funcs.mel_scale.html
new file mode 100644
index 0000000..fd0e98a
--- /dev/null
+++ b/docs/github/html/generated/modulation.audio_utilities.funcs.mel_scale.html
@@ -0,0 +1,118 @@
+<!DOCTYPE html>
+
+<html lang='en'>
+  <head>
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
+    <meta content='Docutils 0.17.1: http://docutils.sourceforge.net/' name='generator'/>
+
+    <title>modulation.audio_utilities.funcs.mel_scale &#8212; modulation 0.0.1 documentation</title>
+    <link href='../_static/pygments.css' rel='stylesheet' type='text/css'/>
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+
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+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
+
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+          <div class='body' role='main'>
+
+            <section id='modulation-audio-utilities-funcs-mel-scale'>
+              <h1>modulation.audio_utilities.funcs.mel_scale<a class='headerlink' href='#modulation-audio-utilities-funcs-mel-scale' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.audio_utilities.funcs.mel_scale'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.audio_utilities.funcs.</span></span><span class='sig-name descname'><span class='pre'>mel_scale</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>x</span></span></em><span class='sig-paren'>)</span><a class='reference internal' href='../_modules/modulation/audio_utilities/funcs.html#mel_scale'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.audio_utilities.funcs.mel_scale' title='Permalink to this definition'>¶</a></dt>
+                <dd>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'><p><strong>x</strong> – </p>
+                    </dd>
+                    <dt class='field-even'>Returns</dt>
+                    <dd class='field-even'><p></p>
+                    </dd>
+                    <dt class='field-odd'>Return type</dt>
+                    <dd class='field-odd'><p></p>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
+            </section>
+
+
+          </div>
+
+        </div>
+      </div>
+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
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+
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+            <li class='toctree-l1'><a class='reference internal' href='modulation.html'>modulation</a></li>
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../index.html'>Documentation overview</a>
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+          <script>document.getElementById('searchbox').style.display = "block"</script>
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+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
+
+      |
+      <a href='../_sources/generated/modulation.audio_utilities.funcs.mel_scale.rst.txt'
+         rel='nofollow'>Page source</a>
+    </div>
+
+
+  </body>
+</html>
\ No newline at end of file
diff --git a/docs/github/html/generated/modulation.audio_utilities.funcs.pre_emphasise.html b/docs/github/html/generated/modulation.audio_utilities.funcs.pre_emphasise.html
new file mode 100644
index 0000000..3f7abb1
--- /dev/null
+++ b/docs/github/html/generated/modulation.audio_utilities.funcs.pre_emphasise.html
@@ -0,0 +1,123 @@
+<!DOCTYPE html>
+
+<html lang='en'>
+  <head>
+    <meta charset='utf-8'/>
+    <meta name='viewport' content='width=device-width, initial-scale=1.0'/>
+    <meta name='generator' content='Docutils 0.17.1: http://docutils.sourceforge.net/'/>
+
+    <title>modulation.audio_utilities.funcs.pre_emphasise &#8212; modulation 0.0.1 documentation</title>
+    <link rel='stylesheet' type='text/css' href='../_static/pygments.css'/>
+    <link rel='stylesheet' type='text/css' href='../_static/alabaster.css'/>
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+    <link rel='canonical' href='aivclab.github.io/modulation/generated/modulation.audio_utilities.funcs.pre_emphasise.html'/>
+    <link rel='index' title='Index' href='../genindex.html'/>
+    <link rel='search' title='Search' href='../search.html'/>
+
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+
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+
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+
+            <section id='modulation-audio-utilities-funcs-pre-emphasise'>
+              <h1>modulation.audio_utilities.funcs.pre_emphasise<a class='headerlink' href='#modulation-audio-utilities-funcs-pre-emphasise' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.audio_utilities.funcs.pre_emphasise'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.audio_utilities.funcs.</span></span><span class='sig-name descname'><span class='pre'>pre_emphasise</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>x</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>coeff</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#float' title='(in Python v3.10)'><span class='pre'>float</span></a></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>0.97</span></span></em><span class='sig-paren'>)</span><a class='reference internal' href='../_modules/modulation/audio_utilities/funcs.html#pre_emphasise'><span class='viewcode-link'><span
+                    class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.audio_utilities.funcs.pre_emphasise' title='Permalink to this definition'>¶</a></dt>
+                <dd>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'>
+                      <ul class='simple'>
+                        <li><p><strong>x</strong> – </p></li>
+                        <li><p><strong>coeff</strong> – </p></li>
+                      </ul>
+                    </dd>
+                    <dt class='field-even'>Returns</dt>
+                    <dd class='field-even'><p></p>
+                    </dd>
+                    <dt class='field-odd'>Return type</dt>
+                    <dd class='field-odd'><p></p>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
+            </section>
+
+
+          </div>
+
+        </div>
+      </div>
+      <div class='sphinxsidebar' role='navigation' aria-label='main navigation'>
+        <div class='sphinxsidebarwrapper'>
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+
+
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='modulation.html'>modulation</a></li>
+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../index.html'>Documentation overview</a>
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+
+      |
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
+
+      |
+      <a href='../_sources/generated/modulation.audio_utilities.funcs.pre_emphasise.rst.txt'
+         rel='nofollow'>Page source</a>
+    </div>
+
+
+  </body>
+</html>
\ No newline at end of file
diff --git a/docs/github/html/generated/modulation.audio_utilities.funcs.pre_emphasise_torch.html b/docs/github/html/generated/modulation.audio_utilities.funcs.pre_emphasise_torch.html
new file mode 100644
index 0000000..e404b54
--- /dev/null
+++ b/docs/github/html/generated/modulation.audio_utilities.funcs.pre_emphasise_torch.html
@@ -0,0 +1,111 @@
+<!DOCTYPE html>
+
+<html lang='en'>
+  <head>
+    <meta charset='utf-8'/>
+    <meta name='viewport' content='width=device-width, initial-scale=1.0'/>
+    <meta name='generator' content='Docutils 0.17.1: http://docutils.sourceforge.net/'/>
+
+    <title>modulation.audio_utilities.funcs.pre_emphasise_torch &#8212; modulation 0.0.1 documentation</title>
+    <link rel='stylesheet' type='text/css' href='../_static/pygments.css'/>
+    <link rel='stylesheet' type='text/css' href='../_static/alabaster.css'/>
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+    <link rel='canonical' href='aivclab.github.io/modulation/generated/modulation.audio_utilities.funcs.pre_emphasise_torch.html'/>
+    <link rel='index' title='Index' href='../genindex.html'/>
+    <link rel='search' title='Search' href='../search.html'/>
+
+    <link rel='stylesheet' href='../_static/custom.css' type='text/css'/>
+
+
+    <meta name='viewport' content='width=device-width, initial-scale=0.9, maximum-scale=0.9'/>
+
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+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
+
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+
+            <section id='modulation-audio-utilities-funcs-pre-emphasise-torch'>
+              <h1>modulation.audio_utilities.funcs.pre_emphasise_torch<a class='headerlink' href='#modulation-audio-utilities-funcs-pre-emphasise-torch' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.audio_utilities.funcs.pre_emphasise_torch'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.audio_utilities.funcs.</span></span><span class='sig-name descname'><span class='pre'>pre_emphasise_torch</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>signal</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>preemph</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#float' title='(in Python v3.10)'><span class='pre'>float</span></a></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>0.97</span></span></em><span class='sig-paren'>)</span><a class='reference internal' href='../_modules/modulation/audio_utilities/funcs.html#pre_emphasise_torch'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a
+                    class='headerlink' href='#modulation.audio_utilities.funcs.pre_emphasise_torch' title='Permalink to this definition'>¶</a></dt>
+                <dd><p>Pre-emphasis on the input signal
+                       :param signal: (time,)
+                       :param preemph:
+                       :return: (time,)</p>
+                </dd>
+              </dl>
+
+            </section>
+
+
+          </div>
+
+        </div>
+      </div>
+      <div class='sphinxsidebar' role='navigation' aria-label='main navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <h1 class='logo'><a href='../index.html'>modulation</a></h1>
+
+
+          <h3>Navigation</h3>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='modulation.html'>modulation</a></li>
+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../index.html'>Documentation overview</a>
+                <ul>
+                </ul>
+              </li>
+            </ul>
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+              </form>
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+
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+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
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+      <a href='../_sources/generated/modulation.audio_utilities.funcs.pre_emphasise_torch.rst.txt'
+         rel='nofollow'>Page source</a>
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+
+
+  </body>
+</html>
\ No newline at end of file
diff --git a/docs/github/html/generated/modulation.audio_utilities.html b/docs/github/html/generated/modulation.audio_utilities.html
new file mode 100644
index 0000000..f66b8bb
--- /dev/null
+++ b/docs/github/html/generated/modulation.audio_utilities.html
@@ -0,0 +1,139 @@
+<!DOCTYPE html>
+
+<html lang='en'>
+  <head>
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
+    <meta content='Docutils 0.17.1: http://docutils.sourceforge.net/' name='generator'/>
+
+    <title>modulation.audio_utilities &#8212; modulation 0.0.1 documentation</title>
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+            <section id='module-modulation.audio_utilities'>
+              <span id='modulation-audio-utilities'></span>
+              <h1>modulation.audio_utilities<a class='headerlink' href='#module-modulation.audio_utilities' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 11-01-2021</p>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.audio_utilities.splitting.html#module-modulation.audio_utilities.splitting' title='modulation.audio_utilities.splitting'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.audio_utilities.splitting</span></code></a></p></td>
+                    <td><p>Created on 15-01-2021</p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
+
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diff --git a/docs/github/html/generated/modulation.audio_utilities.splitting.html b/docs/github/html/generated/modulation.audio_utilities.splitting.html
new file mode 100644
index 0000000..0313bf6
--- /dev/null
+++ b/docs/github/html/generated/modulation.audio_utilities.splitting.html
@@ -0,0 +1,152 @@
+<!DOCTYPE html>
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+              <h1>modulation.audio_utilities.splitting<a class='headerlink' href='#module-modulation.audio_utilities.splitting' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 15-01-2021</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.audio_utilities.splitting.mask_split_speech_silence.html#modulation.audio_utilities.splitting.mask_split_speech_silence' title='modulation.audio_utilities.splitting.mask_split_speech_silence'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>mask_split_speech_silence</span></code></a>(vad, data)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param vad</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
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diff --git a/docs/github/html/generated/modulation.audio_utilities.splitting.mask_split_speech_silence.html b/docs/github/html/generated/modulation.audio_utilities.splitting.mask_split_speech_silence.html
new file mode 100644
index 0000000..5babc07
--- /dev/null
+++ b/docs/github/html/generated/modulation.audio_utilities.splitting.mask_split_speech_silence.html
@@ -0,0 +1,160 @@
+<!DOCTYPE html>
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+            <section id='modulation-audio-utilities-splitting-mask-split-speech-silence'>
+              <h1>modulation.audio_utilities.splitting.mask_split_speech_silence<a class='headerlink' href='#modulation-audio-utilities-splitting-mask-split-speech-silence' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.audio_utilities.splitting.mask_split_speech_silence'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.audio_utilities.splitting.</span></span><span class='sig-name descname'><span class='pre'>mask_split_speech_silence</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>vad</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Sequence' title='(in Python v3.10)'><span class='pre'>Sequence</span></a></span></em>, <em class='sig-param'><span class='n'><span class='pre'>data</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Sequence' title='(in Python v3.10)'><span class='pre'>Sequence</span></a></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span
+                    class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Tuple' title='(in Python v3.10)'><span class='pre'>Tuple</span></a><span class='p'><span class='pre'>[</span></span><a class='reference external' href='https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray' title='(in NumPy v1.23)'><span class='pre'>ndarray</span></a><span class='p'><span class='pre'>,</span></span><span class='w'> </span><a class='reference external' href='https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray' title='(in NumPy v1.23)'><span class='pre'>ndarray</span></a><span class='p'><span class='pre'>]</span></span></span></span><a class='reference internal' href='../_modules/modulation/audio_utilities/splitting.html#mask_split_speech_silence'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a
+                    class='headerlink' href='#modulation.audio_utilities.splitting.mask_split_speech_silence' title='Permalink to this definition'>¶</a></dt>
+                <dd>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'>
+                      <ul class='simple'>
+                        <li><p><strong>vad</strong> – </p></li>
+                        <li><p><strong>data</strong> – </p></li>
+                      </ul>
+                    </dd>
+                    <dt class='field-even'>Returns</dt>
+                    <dd class='field-even'><p></p>
+                    </dd>
+                    <dt class='field-odd'>Return type</dt>
+                    <dd class='field-odd'><p></p>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
+            </section>
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-<span id="modulation-classification-architecture"></span><h1>modulation.classification.architecture<a class="headerlink" href="#module-modulation.classification.architecture" title="Permalink to this heading">¶</a></h1>
-<p>Created on 18-11-2020</p>
-<p class="rubric">Modules</p>
-<table class="autosummary longtable docutils align-default">
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-<td><p>Created on 02-12-2020</p></td>
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-<td><p>Created on 18-11-2020</p></td>
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+              <h1>modulation.classification.architecture<a class='headerlink' href='#module-modulation.classification.architecture' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 18-11-2020</p>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
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+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.classification.architecture.cepstral_a.html#module-modulation.classification.architecture.cepstral_a' title='modulation.classification.architecture.cepstral_a'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.classification.architecture.cepstral_a</span></code></a></p></td>
+                    <td><p>Created on 02-12-2020</p></td>
+                  </tr>
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+                    <td><p>Created on 18-11-2020</p></td>
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--- a/docs/github/html/generated/modulation.classification.architecture.m5.html
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-  <section id="module-modulation.classification.architecture.m5">
-<span id="modulation-classification-architecture-m5"></span><h1>modulation.classification.architecture.m5<a class="headerlink" href="#module-modulation.classification.architecture.m5" title="Permalink to this heading">¶</a></h1>
-<p>Created on 18-11-2020</p>
-<p class="rubric">Classes</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.classification.architecture.m5.M5.html#modulation.classification.architecture.m5.M5" title="modulation.classification.architecture.m5.M5"><code class="xref py py-obj docutils literal notranslate"><span class="pre">M5</span></code></a>([n_input, n_output, stride, n_channel])</p></td>
-<td><p>M5 network architecture described in <a class="reference external" href="https://arxiv.org/pdf/1610.00087.pdf">this paper</a>.</p></td>
-</tr>
-</tbody>
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+              <h1>modulation.classification.architecture.m5<a class='headerlink' href='#module-modulation.classification.architecture.m5' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 18-11-2020</p>
+              <p class='rubric'>Classes</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.classification.architecture.m5.M5.html#modulation.classification.architecture.m5.M5' title='modulation.classification.architecture.m5.M5'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>M5</span></code></a>([n_input, n_output, stride, n_channel])</p></td>
+                    <td><p>M5 network architecture described in <a class='reference external' href='https://arxiv.org/pdf/1610.00087.pdf'>this paper</a>.</p></td>
+                  </tr>
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-<span id="modulation-classification"></span><h1>modulation.classification<a class="headerlink" href="#module-modulation.classification" title="Permalink to this heading">¶</a></h1>
-<p class="rubric">Modules</p>
-<table class="autosummary longtable docutils align-default">
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-<col style="width: 10%" />
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-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.classification.architecture.html#module-modulation.classification.architecture" title="modulation.classification.architecture"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.classification.architecture</span></code></a></p></td>
-<td><p>Created on 18-11-2020</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.classification.metrics.html#module-modulation.classification.metrics" title="modulation.classification.metrics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.classification.metrics</span></code></a></p></td>
-<td><p>Created on 13-01-2021</p></td>
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-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.classification.procedure.html#module-modulation.classification.procedure" title="modulation.classification.procedure"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.classification.procedure</span></code></a></p></td>
-<td><p>Created on 02-12-2020</p></td>
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-<td><p>Created on 18-12-2020</p></td>
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+              <h1>modulation.classification<a class='headerlink' href='#module-modulation.classification' title='Permalink to this heading'>¶</a></h1>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.classification.architecture.html#module-modulation.classification.architecture' title='modulation.classification.architecture'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.classification.architecture</span></code></a></p></td>
+                    <td><p>Created on 18-11-2020</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.classification.metrics.html#module-modulation.classification.metrics' title='modulation.classification.metrics'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.classification.metrics</span></code></a></p></td>
+                    <td><p>Created on 13-01-2021</p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.classification.procedure.html#module-modulation.classification.procedure' title='modulation.classification.procedure'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.classification.procedure</span></code></a></p></td>
+                    <td><p>Created on 02-12-2020</p></td>
+                  </tr>
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+                    <td><p><a class='reference internal' href='modulation.classification.seperation.html#module-modulation.classification.seperation' title='modulation.classification.seperation'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.classification.seperation</span></code></a></p></td>
+                    <td><p>Created on 18-12-2020</p></td>
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-<span id="modulation-classification-metrics"></span><h1>modulation.classification.metrics<a class="headerlink" href="#module-modulation.classification.metrics" title="Permalink to this heading">¶</a></h1>
-<p>Created on 13-01-2021</p>
-<p class="rubric">Modules</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
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-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.classification.metrics.lwlrap.html#module-modulation.classification.metrics.lwlrap" title="modulation.classification.metrics.lwlrap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.classification.metrics.lwlrap</span></code></a></p></td>
-<td><p>Created on 03-12-2020</p></td>
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+              <h1>modulation.classification.metrics<a class='headerlink' href='#module-modulation.classification.metrics' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 13-01-2021</p>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.classification.metrics.lwlrap.html#module-modulation.classification.metrics.lwlrap' title='modulation.classification.metrics.lwlrap'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.classification.metrics.lwlrap</span></code></a></p></td>
+                    <td><p>Created on 03-12-2020</p></td>
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diff --git a/docs/github/html/generated/modulation.classification.metrics.lwlrap.calculate_overall_lwlrap_sklearn.html b/docs/github/html/generated/modulation.classification.metrics.lwlrap.calculate_overall_lwlrap_sklearn.html
new file mode 100644
index 0000000..3994582
--- /dev/null
+++ b/docs/github/html/generated/modulation.classification.metrics.lwlrap.calculate_overall_lwlrap_sklearn.html
@@ -0,0 +1,157 @@
+<!DOCTYPE html>
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+            <section id='modulation-classification-metrics-lwlrap-calculate-overall-lwlrap-sklearn'>
+              <h1>modulation.classification.metrics.lwlrap.calculate_overall_lwlrap_sklearn<a class='headerlink' href='#modulation-classification-metrics-lwlrap-calculate-overall-lwlrap-sklearn' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.classification.metrics.lwlrap.calculate_overall_lwlrap_sklearn'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.classification.metrics.lwlrap.</span></span><span class='sig-name descname'><span class='pre'>calculate_overall_lwlrap_sklearn</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>truth</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>scores</span></span></em><span class='sig-paren'>)</span><a class='reference internal' href='../_modules/modulation/classification/metrics/lwlrap.html#calculate_overall_lwlrap_sklearn'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.classification.metrics.lwlrap.calculate_overall_lwlrap_sklearn' title='Permalink to this definition'>¶</a></dt>
+                <dd><p>Calculate the overall lwlrap using sklearn.metrics.lrap.</p>
+                </dd>
+              </dl>
+
+            </section>
+
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+
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+                <li class='toctree-l2'><a class='reference internal' href='modulation.torch_utilities.html'>modulation.torch_utilities</a></li>
+              </ul>
+            </li>
+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
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+                                  <li>Previous: <a href='modulation.classification.metrics.lwlrap.html' title='previous chapter'>modulation.classification.metrics.lwlrap</a></li>
+                                  <li>Next: <a href='modulation.classification.metrics.lwlrap.calculate_per_class_lwlrap.html' title='next chapter'>modulation.classification.metrics.lwlrap.calculate_per_class_lwlrap</a></li>
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-            
-  <section id="module-modulation.classification.metrics.lwlrap">
-<span id="modulation-classification-metrics-lwlrap"></span><h1>modulation.classification.metrics.lwlrap<a class="headerlink" href="#module-modulation.classification.metrics.lwlrap" title="Permalink to this heading">¶</a></h1>
-<p>Created on 03-12-2020</p>
-<p class="rubric">Functions</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.classification.metrics.lwlrap.one_sample_positive_class_precisions.html#modulation.classification.metrics.lwlrap.one_sample_positive_class_precisions" title="modulation.classification.metrics.lwlrap.one_sample_positive_class_precisions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">one_sample_positive_class_precisions</span></code></a>(scores, ...)</p></td>
-<td><p>Reference implementation of l$omega$lrap both natively and using sklearn.metrics.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.classification.metrics.lwlrap.calculate_per_class_lwlrap.html#modulation.classification.metrics.lwlrap.calculate_per_class_lwlrap" title="modulation.classification.metrics.lwlrap.calculate_per_class_lwlrap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calculate_per_class_lwlrap</span></code></a>(truth, scores)</p></td>
-<td><p>Calculate label-weighted label-ranking average precision.</p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Classes</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.classification.metrics.lwlrap.lwlrap_accumulator.html#modulation.classification.metrics.lwlrap.lwlrap_accumulator" title="modulation.classification.metrics.lwlrap.lwlrap_accumulator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lwlrap_accumulator</span></code></a>()</p></td>
-<td><p>Accumulate batches of test samples into per-class and overall lwlrap.</p></td>
-</tr>
-</tbody>
-</table>
-</section>
+    <link href='../_static/pygments.css' rel='stylesheet' type='text/css'/>
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+            <section id='module-modulation.classification.metrics.lwlrap'>
+              <span id='modulation-classification-metrics-lwlrap'></span>
+              <h1>modulation.classification.metrics.lwlrap<a class='headerlink' href='#module-modulation.classification.metrics.lwlrap' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 03-12-2020</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.classification.metrics.lwlrap.one_sample_positive_class_precisions.html#modulation.classification.metrics.lwlrap.one_sample_positive_class_precisions' title='modulation.classification.metrics.lwlrap.one_sample_positive_class_precisions'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>one_sample_positive_class_precisions</span></code></a>(scores, ...)</p></td>
+                    <td><p>Reference implementation of l$omega$lrap both natively and using sklearn.metrics.</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.classification.metrics.lwlrap.calculate_per_class_lwlrap.html#modulation.classification.metrics.lwlrap.calculate_per_class_lwlrap' title='modulation.classification.metrics.lwlrap.calculate_per_class_lwlrap'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>calculate_per_class_lwlrap</span></code></a>(truth, scores)</p></td>
+                    <td><p>Calculate label-weighted label-ranking average precision.</p></td>
+                  </tr>
+                </tbody>
+              </table>
+              <p class='rubric'>Classes</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.classification.metrics.lwlrap.lwlrap_accumulator.html#modulation.classification.metrics.lwlrap.lwlrap_accumulator' title='modulation.classification.metrics.lwlrap.lwlrap_accumulator'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>lwlrap_accumulator</span></code></a>()</p></td>
+                    <td><p>Accumulate batches of test samples into per-class and overall lwlrap.</p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
 
 
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@@ -1,167 +1,168 @@
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-  <section id="module-modulation.classification.procedure">
-<span id="modulation-classification-procedure"></span><h1>modulation.classification.procedure<a class="headerlink" href="#module-modulation.classification.procedure" title="Permalink to this heading">¶</a></h1>
-<p>Created on 02-12-2020</p>
-<p class="rubric">Functions</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.classification.procedure.single_epoch_fitting.html#modulation.classification.procedure.single_epoch_fitting" title="modulation.classification.procedure.single_epoch_fitting"><code class="xref py py-obj docutils literal notranslate"><span class="pre">single_epoch_fitting</span></code></a>(model, optimiser, ...)</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param model</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.classification.procedure.single_epoch_evaluation.html#modulation.classification.procedure.single_epoch_evaluation" title="modulation.classification.procedure.single_epoch_evaluation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">single_epoch_evaluation</span></code></a>(model, ...[, epoch, ...])</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param model</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-</tbody>
-</table>
-</section>
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+            <section id='module-modulation.classification.procedure'>
+              <span id='modulation-classification-procedure'></span>
+              <h1>modulation.classification.procedure<a class='headerlink' href='#module-modulation.classification.procedure' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 02-12-2020</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.classification.procedure.single_epoch_fitting.html#modulation.classification.procedure.single_epoch_fitting' title='modulation.classification.procedure.single_epoch_fitting'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>single_epoch_fitting</span></code></a>(model, optimiser, ...)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param model</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.classification.procedure.single_epoch_evaluation.html#modulation.classification.procedure.single_epoch_evaluation' title='modulation.classification.procedure.single_epoch_evaluation'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>single_epoch_evaluation</span></code></a>(model, ...[, epoch, ...])</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param model</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
 
 
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+              <span id='modulation-classification-procs'></span>
+              <h1>modulation.classification.procs<a class='headerlink' href='#module-modulation.classification.procs' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 02-12-2020</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.classification.procs.single_epoch_evaluation.html#modulation.classification.procs.single_epoch_evaluation' title='modulation.classification.procs.single_epoch_evaluation'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>single_epoch_evaluation</span></code></a>(model, ...[, epoch, ...])</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param model</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.classification.procs.single_epoch_fitting.html#modulation.classification.procs.single_epoch_fitting' title='modulation.classification.procs.single_epoch_fitting'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>single_epoch_fitting</span></code></a>(model, optimiser, ...)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param model</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
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diff --git a/docs/github/html/generated/modulation.classification.procs.single_epoch_evaluation.html b/docs/github/html/generated/modulation.classification.procs.single_epoch_evaluation.html
new file mode 100644
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+<!DOCTYPE html>
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+            <section id='modulation-classification-procs-single-epoch-evaluation'>
+              <h1>modulation.classification.procs.single_epoch_evaluation<a class='headerlink' href='#modulation-classification-procs-single-epoch-evaluation' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.classification.procs.single_epoch_evaluation'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.classification.procs.</span></span><span class='sig-name descname'><span class='pre'>single_epoch_evaluation</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>model</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><span class='pre'>Module</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>evaluation_loader</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><span class='pre'>DataLoader</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>subset</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><span class='pre'>SplitEnum</span></span></em>, <em class='sig-param'><span class='o'><span class='pre'>*</span></span></em>, <em class='sig-param'><span
+                    class='n'><span class='pre'>epoch</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Optional' title='(in Python v3.10)'><span class='pre'>Optional</span></a><span class='p'><span class='pre'>[</span></span><a class='reference external' href='https://docs.python.org/3/library/functions.html#int' title='(in Python v3.10)'><span class='pre'>int</span></a><span class='p'><span class='pre'>]</span></span></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>None</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>writer</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           href='https://docs.python.org/3/library/typing.html#typing.Optional'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           title='(in Python v3.10)'><span class='pre'>Optional</span></a><span class='p'><span
+                    class='pre'>[</span></span><span class='pre'>Writer</span><span class='p'><span class='pre'>]</span></span></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>None</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>device</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><span class='pre'>device</span></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>device(type='cuda')</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://docs.python.org/3/library/functions.html#float' title='(in Python v3.10)'><span class='pre'>float</span></a></span></span><a
+                    class='reference internal' href='../_modules/modulation/classification/procs.html#single_epoch_evaluation'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.classification.procs.single_epoch_evaluation' title='Permalink to this definition'>¶</a></dt>
+                <dd>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'>
+                      <ul class='simple'>
+                        <li><p><strong>model</strong> – </p></li>
+                        <li><p><strong>evaluation_loader</strong> – </p></li>
+                        <li><p><strong>subset</strong> – </p></li>
+                        <li><p><strong>epoch</strong> – </p></li>
+                        <li><p><strong>writer</strong> – </p></li>
+                        <li><p><strong>device</strong> – </p></li>
+                      </ul>
+                    </dd>
+                    <dt class='field-even'>Returns</dt>
+                    <dd class='field-even'><p></p>
+                    </dd>
+                    <dt class='field-odd'>Return type</dt>
+                    <dd class='field-odd'><p></p>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
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diff --git a/docs/github/html/generated/modulation.classification.procs.single_epoch_fitting.html b/docs/github/html/generated/modulation.classification.procs.single_epoch_fitting.html
new file mode 100644
index 0000000..70da5b7
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@@ -0,0 +1,163 @@
+<!DOCTYPE html>
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+            <section id='modulation-classification-procs-single-epoch-fitting'>
+              <h1>modulation.classification.procs.single_epoch_fitting<a class='headerlink' href='#modulation-classification-procs-single-epoch-fitting' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.classification.procs.single_epoch_fitting'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.classification.procs.</span></span><span class='sig-name descname'><span class='pre'>single_epoch_fitting</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>model</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><span class='pre'>Module</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>optimiser</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>train_loader_</span></span></em>, <em class='sig-param'><span class='o'><span class='pre'>*</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>epoch</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Optional' title='(in Python v3.10)'><span
+                    class='pre'>Optional</span></a><span class='p'><span class='pre'>[</span></span><a class='reference external' href='https://docs.python.org/3/library/functions.html#int' title='(in Python v3.10)'><span class='pre'>int</span></a><span class='p'><span class='pre'>]</span></span></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>None</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>writer</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Optional' title='(in Python v3.10)'><span class='pre'>Optional</span></a><span class='p'><span class='pre'>[</span></span><span class='pre'>Writer</span><span class='p'><span class='pre'>]</span></span></span><span class='w'> </span><span class='o'><span
+                    class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>None</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>device_</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><span class='pre'>device</span></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>device(type='cuda')</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://docs.python.org/3/library/constants.html#None' title='(in Python v3.10)'><span class='pre'>None</span></a></span></span><a class='reference internal' href='../_modules/modulation/classification/procs.html#single_epoch_fitting'><span class='viewcode-link'><span
+                    class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.classification.procs.single_epoch_fitting' title='Permalink to this definition'>¶</a></dt>
+                <dd>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'>
+                      <ul class='simple'>
+                        <li><p><strong>model</strong> – </p></li>
+                        <li><p><strong>optimiser</strong> – </p></li>
+                        <li><p><strong>train_loader</strong> – </p></li>
+                        <li><p><strong>epoch</strong> – </p></li>
+                        <li><p><strong>writer</strong> – </p></li>
+                        <li><p><strong>device</strong> – </p></li>
+                      </ul>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
+            </section>
+
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+                              <li>Previous: <a href='modulation.classification.procs.single_epoch_evaluation.html' title='previous chapter'>modulation.classification.procs.single_epoch_evaluation</a></li>
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-<p>Created on 18-12-2020</p>
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+              <h1>modulation.classification.seperation<a class='headerlink' href='#module-modulation.classification.seperation' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 18-12-2020</p>
+              <p class='rubric'>Modules</p>
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+                    <td><p>Created on 29/03/2020</p></td>
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diff --git a/docs/github/html/generated/modulation.data.audio.speech.html b/docs/github/html/generated/modulation.data.audio.speech.html
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-<span id="modulation-data-audio-speech"></span><h1>modulation.data.audio.speech<a class="headerlink" href="#module-modulation.data.audio.speech" title="Permalink to this heading">¶</a></h1>
-<p class="rubric">Modules</p>
-<table class="autosummary longtable docutils align-default">
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-<col style="width: 10%" />
-<col style="width: 90%" />
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+              <h1>modulation.data.audio.speech<a class='headerlink' href='#module-modulation.data.audio.speech' title='Permalink to this heading'>¶</a></h1>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
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+                    <td><p><a class='reference internal' href='modulation.data.audio.speech.seperation.html#module-modulation.data.audio.speech.seperation' title='modulation.data.audio.speech.seperation'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.data.audio.speech.seperation</span></code></a></p></td>
+                    <td><p>Created on 04-05-2021</p></td>
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diff --git a/docs/github/html/generated/modulation.data.audio.speech.seperation.html b/docs/github/html/generated/modulation.data.audio.speech.seperation.html
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+              <h1>modulation.data.audio.speech.seperation<a class='headerlink' href='#module-modulation.data.audio.speech.seperation' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 04-05-2021</p>
+              <p class='rubric'>Modules</p>
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+              <p>description</p>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
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+                    <td><p><a class='reference internal' href='modulation.data.audio.html#module-modulation.data.audio' title='modulation.data.audio'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.data.audio</span></code></a></p></td>
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diff --git a/docs/github/html/generated/modulation.data.signal_generator.html b/docs/github/html/generated/modulation.data.signal_generator.html
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--- a/docs/github/html/generated/modulation.data.signal_generator.html
+++ b/docs/github/html/generated/modulation.data.signal_generator.html
@@ -1,174 +1,175 @@
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-            
-  <section id="module-modulation.data.signal_generator">
-<span id="modulation-data-signal-generator"></span><h1>modulation.data.signal_generator<a class="headerlink" href="#module-modulation.data.signal_generator" title="Permalink to this heading">¶</a></h1>
-<p>Created on 29/03/2020</p>
-<p class="rubric">Functions</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.data.signal_generator.multifreq.html#modulation.data.signal_generator.multifreq" title="modulation.data.signal_generator.multifreq"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multifreq</span></code></a>(x)</p></td>
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-</tr>
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-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Classes</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.data.signal_generator.SignalGenerator.html#modulation.data.signal_generator.SignalGenerator" title="modulation.data.signal_generator.SignalGenerator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SignalGenerator</span></code></a>(*funcs[, delta_time])</p></td>
-<td><p>description</p></td>
-</tr>
-</tbody>
-</table>
-</section>
+    <link href='../_static/pygments.css' rel='stylesheet' type='text/css'/>
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+          <div class='body' role='main'>
+
+            <section id='module-modulation.data.signal_generator'>
+              <span id='modulation-data-signal-generator'></span>
+              <h1>modulation.data.signal_generator<a class='headerlink' href='#module-modulation.data.signal_generator' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 29/03/2020</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.data.signal_generator.multifreq.html#modulation.data.signal_generator.multifreq' title='modulation.data.signal_generator.multifreq'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>multifreq</span></code></a>(x)</p></td>
+                    <td><p></p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.data.signal_generator.triangle.html#modulation.data.signal_generator.triangle' title='modulation.data.signal_generator.triangle'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>triangle</span></code></a>(x[, section_length])</p></td>
+                    <td><p></p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.data.signal_generator.sawtooth.html#modulation.data.signal_generator.sawtooth' title='modulation.data.signal_generator.sawtooth'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>sawtooth</span></code></a>(x[, section_length])</p></td>
+                    <td><p></p></td>
+                  </tr>
+                </tbody>
+              </table>
+              <p class='rubric'>Classes</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.data.signal_generator.SignalGenerator.html#modulation.data.signal_generator.SignalGenerator' title='modulation.data.signal_generator.SignalGenerator'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>SignalGenerator</span></code></a>(*funcs[, delta_time])</p></td>
+                    <td><p>description</p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
 
 
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diff --git a/docs/github/html/generated/modulation.data.signal_generator.multifreq.html b/docs/github/html/generated/modulation.data.signal_generator.multifreq.html
index e77127d..36e75ec 100644
--- a/docs/github/html/generated/modulation.data.signal_generator.multifreq.html
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+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.data.signal_generator.multifreq'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.data.signal_generator.</span></span><span class='sig-name descname'><span class='pre'>multifreq</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>x</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray' title='(in NumPy v1.23)'><span class='pre'>ndarray</span></a></span></span><a class='reference internal' href='../_modules/modulation/data/signal_generator.html#multifreq'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.data.signal_generator.multifreq' title='Permalink to this definition'>¶</a></dt>
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diff --git a/docs/github/html/generated/modulation.data.signal_generator.sawtooth.html b/docs/github/html/generated/modulation.data.signal_generator.sawtooth.html
index 7975af8..44c39d6 100644
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@@ -1,150 +1,150 @@
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-<dl class="py function">
-<dt class="sig sig-object py" id="modulation.data.signal_generator.sawtooth">
-<span class="sig-prename descclassname"><span class="pre">modulation.data.signal_generator.</span></span><span class="sig-name descname"><span class="pre">sawtooth</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">section_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.23)"><span class="pre">ndarray</span></a></span></span><a class="reference internal" href="../_modules/modulation/data/signal_generator.html#sawtooth"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#modulation.data.signal_generator.sawtooth" title="Permalink to this definition">¶</a></dt>
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+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.data.signal_generator.sawtooth'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.data.signal_generator.</span></span><span class='sig-name descname'><span class='pre'>sawtooth</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>x</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>section_length</span></span><span class='o'><span class='pre'>=</span></span><span class='default_value'><span class='pre'>0.5</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray' title='(in NumPy v1.23)'><span class='pre'>ndarray</span></a></span></span><a class='reference internal' href='../_modules/modulation/data/signal_generator.html#sawtooth'><span class='viewcode-link'><span
+                    class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.data.signal_generator.sawtooth' title='Permalink to this definition'>¶</a></dt>
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-<dl class="py function">
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-<span class="sig-prename descclassname"><span class="pre">modulation.data.signal_generator.</span></span><span class="sig-name descname"><span class="pre">triangle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">section_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.23)"><span class="pre">ndarray</span></a></span></span><a class="reference internal" href="../_modules/modulation/data/signal_generator.html#triangle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#modulation.data.signal_generator.triangle" title="Permalink to this definition">¶</a></dt>
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+              <h1>modulation.data.signal_generator.triangle<a class='headerlink' href='#modulation-data-signal-generator-triangle' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.data.signal_generator.triangle'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.data.signal_generator.</span></span><span class='sig-name descname'><span class='pre'>triangle</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>x</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>section_length</span></span><span class='o'><span class='pre'>=</span></span><span class='default_value'><span class='pre'>0.5</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray' title='(in NumPy v1.23)'><span class='pre'>ndarray</span></a></span></span><a class='reference internal' href='../_modules/modulation/data/signal_generator.html#triangle'><span class='viewcode-link'><span
+                    class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.data.signal_generator.triangle' title='Permalink to this definition'>¶</a></dt>
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diff --git a/docs/github/html/generated/modulation.denoising.finger_print.html b/docs/github/html/generated/modulation.denoising.finger_print.html
new file mode 100644
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diff --git a/docs/github/html/generated/modulation.denoising.html b/docs/github/html/generated/modulation.denoising.html
new file mode 100644
index 0000000..2566d9b
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@@ -0,0 +1,143 @@
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+              <h1>modulation.denoising<a class='headerlink' href='#module-modulation.denoising' title='Permalink to this heading'>¶</a></h1>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.denoising.band_pass.html#module-modulation.denoising.band_pass' title='modulation.denoising.band_pass'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.denoising.band_pass</span></code></a></p></td>
+                    <td><p></p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.denoising.finger_print.html#module-modulation.denoising.finger_print' title='modulation.denoising.finger_print'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.denoising.finger_print</span></code></a></p></td>
+                    <td><p></p></td>
+                  </tr>
+                </tbody>
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diff --git a/docs/github/html/generated/modulation.entry_points.cli.ModulationCLI.html b/docs/github/html/generated/modulation.entry_points.cli.ModulationCLI.html
index 0dbe89a..35283b3 100644
--- a/docs/github/html/generated/modulation.entry_points.cli.ModulationCLI.html
+++ b/docs/github/html/generated/modulation.entry_points.cli.ModulationCLI.html
@@ -1,194 +1,201 @@
-
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-  <section id="modulation-entry-points-cli-modulationcli">
-<h1>modulation.entry_points.cli.ModulationCLI<a class="headerlink" href="#modulation-entry-points-cli-modulationcli" title="Permalink to this heading">¶</a></h1>
-<dl class="py class">
-<dt class="sig sig-object py" id="modulation.entry_points.cli.ModulationCLI">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">modulation.entry_points.cli.</span></span><span class="sig-name descname"><span class="pre">ModulationCLI</span></span><a class="reference internal" href="../_modules/modulation/entry_points/cli.html#ModulationCLI"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#modulation.entry_points.cli.ModulationCLI" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.10)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
-<dl class="py method">
-<dt class="sig sig-object py" id="modulation.entry_points.cli.ModulationCLI.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#modulation.entry_points.cli.ModulationCLI.__init__" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<p class="rubric">Methods</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#modulation.entry_points.cli.ModulationCLI.__init__" title="modulation.entry_points.cli.ModulationCLI.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>()</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#modulation.entry_points.cli.ModulationCLI.run" title="modulation.entry_points.cli.ModulationCLI.run"><code class="xref py py-obj docutils literal notranslate"><span class="pre">run</span></code></a>()</p></td>
-<td><p>description</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#modulation.entry_points.cli.ModulationCLI.sponsors" title="modulation.entry_points.cli.ModulationCLI.sponsors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sponsors</span></code></a>()</p></td>
-<td><p>description</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#modulation.entry_points.cli.ModulationCLI.version" title="modulation.entry_points.cli.ModulationCLI.version"><code class="xref py py-obj docutils literal notranslate"><span class="pre">version</span></code></a>()</p></td>
-<td><p>Prints the version of this installation.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt class="sig sig-object py" id="modulation.entry_points.cli.ModulationCLI.run">
-<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.10)"><span class="pre">None</span></a></span></span><a class="reference internal" href="../_modules/modulation/entry_points/cli.html#ModulationCLI.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#modulation.entry_points.cli.ModulationCLI.run" title="Permalink to this definition">¶</a></dt>
-<dd><p>description</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="modulation.entry_points.cli.ModulationCLI.sponsors">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">sponsors</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.10)"><span class="pre">None</span></a></span></span><a class="reference internal" href="../_modules/modulation/entry_points/cli.html#ModulationCLI.sponsors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#modulation.entry_points.cli.ModulationCLI.sponsors" title="Permalink to this definition">¶</a></dt>
-<dd><p>description</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="modulation.entry_points.cli.ModulationCLI.version">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">version</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.10)"><span class="pre">None</span></a></span></span><a class="reference internal" href="../_modules/modulation/entry_points/cli.html#ModulationCLI.version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#modulation.entry_points.cli.ModulationCLI.version" title="Permalink to this definition">¶</a></dt>
-<dd><p>Prints the version of this installation.</p>
-</dd></dl>
-
-</dd></dl>
-
-</section>
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+            <section id='modulation-entry-points-cli-modulationcli'>
+              <h1>modulation.entry_points.cli.ModulationCLI<a class='headerlink' href='#modulation-entry-points-cli-modulationcli' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py class'>
+                <dt class='sig sig-object py' id='modulation.entry_points.cli.ModulationCLI'>
+                  <em class='property'><span class='pre'>class</span><span class='w'> </span></em><span class='sig-prename descclassname'><span class='pre'>modulation.entry_points.cli.</span></span><span class='sig-name descname'><span class='pre'>ModulationCLI</span></span><a class='reference internal' href='../_modules/modulation/entry_points/cli.html#ModulationCLI'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.entry_points.cli.ModulationCLI' title='Permalink to this definition'>¶</a></dt>
+                <dd><p>Bases: <a class='reference external' href='https://docs.python.org/3/library/functions.html#object' title='(in Python v3.10)'><code class='xref py py-class docutils literal notranslate'><span class='pre'>object</span></code></a></p>
+                  <dl class='py method'>
+                    <dt class='sig sig-object py' id='modulation.entry_points.cli.ModulationCLI.__init__'>
+                      <span class='sig-name descname'><span class='pre'>__init__</span></span><span class='sig-paren'>(</span><span class='sig-paren'>)</span><a class='headerlink' href='#modulation.entry_points.cli.ModulationCLI.__init__' title='Permalink to this definition'>¶</a></dt>
+                    <dd></dd>
+                  </dl>
+
+                  <p class='rubric'>Methods</p>
+                  <table class='autosummary longtable docutils align-default'>
+                    <colgroup>
+                      <col style='width: 10%'/>
+                      <col style='width: 90%'/>
+                    </colgroup>
+                    <tbody>
+                      <tr class='row-odd'>
+                        <td><p><a class='reference internal' href='#modulation.entry_points.cli.ModulationCLI.__init__' title='modulation.entry_points.cli.ModulationCLI.__init__'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>__init__</span></code></a>()</p></td>
+                        <td><p></p></td>
+                      </tr>
+                      <tr class='row-even'>
+                        <td><p><a class='reference internal' href='#modulation.entry_points.cli.ModulationCLI.run' title='modulation.entry_points.cli.ModulationCLI.run'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>run</span></code></a>()</p></td>
+                        <td><p>description</p></td>
+                      </tr>
+                      <tr class='row-odd'>
+                        <td><p><a class='reference internal' href='#modulation.entry_points.cli.ModulationCLI.sponsors' title='modulation.entry_points.cli.ModulationCLI.sponsors'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>sponsors</span></code></a>()</p></td>
+                        <td><p>description</p></td>
+                      </tr>
+                      <tr class='row-even'>
+                        <td><p><a class='reference internal' href='#modulation.entry_points.cli.ModulationCLI.version' title='modulation.entry_points.cli.ModulationCLI.version'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>version</span></code></a>()</p></td>
+                        <td><p>Prints the version of this installation.</p></td>
+                      </tr>
+                    </tbody>
+                  </table>
+                  <dl class='py method'>
+                    <dt class='sig sig-object py' id='modulation.entry_points.cli.ModulationCLI.run'>
+                      <span class='sig-name descname'><span class='pre'>run</span></span><span class='sig-paren'>(</span><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://docs.python.org/3/library/constants.html#None' title='(in Python v3.10)'><span class='pre'>None</span></a></span></span><a class='reference internal' href='../_modules/modulation/entry_points/cli.html#ModulationCLI.run'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.entry_points.cli.ModulationCLI.run' title='Permalink to this definition'>¶</a></dt>
+                    <dd><p>description</p>
+                    </dd>
+                  </dl>
+
+                  <dl class='py method'>
+                    <dt class='sig sig-object py' id='modulation.entry_points.cli.ModulationCLI.sponsors'>
+                      <em class='property'><span class='pre'>static</span><span class='w'> </span></em><span class='sig-name descname'><span class='pre'>sponsors</span></span><span class='sig-paren'>(</span><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://docs.python.org/3/library/constants.html#None' title='(in Python v3.10)'><span class='pre'>None</span></a></span></span><a class='reference internal' href='../_modules/modulation/entry_points/cli.html#ModulationCLI.sponsors'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.entry_points.cli.ModulationCLI.sponsors' title='Permalink to this definition'>¶</a></dt>
+                    <dd><p>description</p>
+                    </dd>
+                  </dl>
+
+                  <dl class='py method'>
+                    <dt class='sig sig-object py' id='modulation.entry_points.cli.ModulationCLI.version'>
+                      <em class='property'><span class='pre'>static</span><span class='w'> </span></em><span class='sig-name descname'><span class='pre'>version</span></span><span class='sig-paren'>(</span><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://docs.python.org/3/library/constants.html#None' title='(in Python v3.10)'><span class='pre'>None</span></a></span></span><a class='reference internal' href='../_modules/modulation/entry_points/cli.html#ModulationCLI.version'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.entry_points.cli.ModulationCLI.version' title='Permalink to this definition'>¶</a></dt>
+                    <dd><p>Prints the version of this installation.</p>
+                    </dd>
+                  </dl>
+
+                </dd>
+              </dl>
+
+            </section>
 
 
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+              <form action='../search.html' class='search' method='get'>
+                <input aria-labelledby='searchlabel' autocapitalize='off' autocomplete='off' autocorrect='off' name='q' spellcheck='false' type='text'/>
+                <input type='submit' value='Go'/>
+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
 
 
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+      <div class='clearer'></div>
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+    <div class='footer'>
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-      
+
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-      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
-      
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
+
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-      <a href="../_sources/generated/modulation.entry_points.cli.ModulationCLI.rst.txt"
-          rel="nofollow">Page source</a>
+      <a href='../_sources/generated/modulation.entry_points.cli.ModulationCLI.rst.txt'
+         rel='nofollow'>Page source</a>
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\ No newline at end of file
diff --git a/docs/github/html/generated/modulation.entry_points.cli.html b/docs/github/html/generated/modulation.entry_points.cli.html
index 2f6f18e..e85d72f 100644
--- a/docs/github/html/generated/modulation.entry_points.cli.html
+++ b/docs/github/html/generated/modulation.entry_points.cli.html
@@ -1,178 +1,180 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
+    <meta content='Docutils 0.17.1: http://docutils.sourceforge.net/' name='generator'/>
 
     <title>modulation.entry_points.cli &#8212; modulation 0.0.1 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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-   
-  <link rel="stylesheet" href="../_static/custom.css" type="text/css" />
-  
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-  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
-
-  </head><body>
-  
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-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          
-
-          <div class="body" role="main">
-            
-  <section id="module-modulation.entry_points.cli">
-<span id="modulation-entry-points-cli"></span><h1>modulation.entry_points.cli<a class="headerlink" href="#module-modulation.entry_points.cli" title="Permalink to this heading">¶</a></h1>
-<p>Created on 09/02/2020</p>
-<p class="rubric">Functions</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.entry_points.cli.draw_cli_header.html#modulation.entry_points.cli.draw_cli_header" title="modulation.entry_points.cli.draw_cli_header"><code class="xref py py-obj docutils literal notranslate"><span class="pre">draw_cli_header</span></code></a>(*[, title, font])</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param title</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.entry_points.cli.main.html#modulation.entry_points.cli.main" title="modulation.entry_points.cli.main"><code class="xref py py-obj docutils literal notranslate"><span class="pre">main</span></code></a>(*[, always_draw_header])</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param always_draw_header</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Classes</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.entry_points.cli.ModulationCLI.html#modulation.entry_points.cli.ModulationCLI" title="modulation.entry_points.cli.ModulationCLI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ModulationCLI</span></code></a>()</p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-</section>
+    <link href='../_static/pygments.css' rel='stylesheet' type='text/css'/>
+    <link href='../_static/alabaster.css' rel='stylesheet' type='text/css'/>
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+    <link href='Aivclab.github.io/modulation/generated/modulation.entry_points.cli.html' rel='canonical'/>
+    <link href='../genindex.html' rel='index' title='Index'/>
+    <link href='../search.html' rel='search' title='Search'/>
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+
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+
+
+    <meta content='width=device-width, initial-scale=0.9, maximum-scale=0.9' name='viewport'/>
+
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+  <body>
+
+
+    <div class='document'>
+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
+
+
+          <div class='body' role='main'>
+
+            <section id='module-modulation.entry_points.cli'>
+              <span id='modulation-entry-points-cli'></span>
+              <h1>modulation.entry_points.cli<a class='headerlink' href='#module-modulation.entry_points.cli' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 09/02/2020</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.entry_points.cli.draw_cli_header.html#modulation.entry_points.cli.draw_cli_header' title='modulation.entry_points.cli.draw_cli_header'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>draw_cli_header</span></code></a>(*[, title, font])</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param title</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.entry_points.cli.main.html#modulation.entry_points.cli.main' title='modulation.entry_points.cli.main'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>main</span></code></a>(*[, always_draw_header])</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param always_draw_header</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                </tbody>
+              </table>
+              <p class='rubric'>Classes</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.entry_points.cli.ModulationCLI.html#modulation.entry_points.cli.ModulationCLI' title='modulation.entry_points.cli.ModulationCLI'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>ModulationCLI</span></code></a>()</p></td>
+                    <td><p></p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
 
 
           </div>
-          
+
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-<li class="toctree-l4"><a class="reference internal" href="modulation.entry_points.cli.ModulationCLI.html">modulation.entry_points.cli.ModulationCLI</a></li>
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+                <li class='toctree-l2'><a class='reference internal' href='modulation.classification.html'>modulation.classification</a></li>
+                <li class='toctree-l2'><a class='reference internal' href='modulation.data.html'>modulation.data</a></li>
+                <li class='toctree-l2 current'><a class='reference internal' href='modulation.entry_points.html'>modulation.entry_points</a>
+                  <ul class='current'>
+                    <li class='toctree-l3 current'><a class='current reference internal' href='#'>modulation.entry_points.cli</a>
+                      <ul>
+                        <li class='toctree-l4'><a class='reference internal' href='modulation.entry_points.cli.draw_cli_header.html'>modulation.entry_points.cli.draw_cli_header</a></li>
+                        <li class='toctree-l4'><a class='reference internal' href='modulation.entry_points.cli.main.html'>modulation.entry_points.cli.main</a></li>
+                        <li class='toctree-l4'><a class='reference internal' href='modulation.entry_points.cli.ModulationCLI.html'>modulation.entry_points.cli.ModulationCLI</a></li>
+                      </ul>
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+                    <li class='toctree-l3'><a class='reference internal' href='modulation.entry_points.tensorboard_entry_point.html'>modulation.entry_points.tensorboard_entry_point</a></li>
+                  </ul>
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+                <li class='toctree-l2'><a class='reference internal' href='modulation.signal_utilities.html'>modulation.signal_utilities</a></li>
+                <li class='toctree-l2'><a class='reference internal' href='modulation.torch_utilities.html'>modulation.torch_utilities</a></li>
+                <li class='toctree-l2'><a class='reference internal' href='modulation.visualisation.html'>modulation.visualisation</a></li>
+              </ul>
+            </li>
+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../index.html'>Documentation overview</a>
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+                  <li><a href='modulation.html'>modulation</a>
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+                      <li><a href='modulation.entry_points.html'>modulation.entry_points</a>
+                        <ul>
+                          <li>Previous: <a href='modulation.entry_points.html' title='previous chapter'>modulation.entry_points</a></li>
+                          <li>Next: <a href='modulation.entry_points.cli.draw_cli_header.html' title='next chapter'>modulation.entry_points.cli.draw_cli_header</a></li>
+                        </ul>
+                      </li>
+                    </ul>
+                  </li>
+                </ul>
+              </li>
+            </ul>
+          </div>
+          <div id='searchbox' role='search' style='display: none'>
+            <h3 id='searchlabel'>Quick search</h3>
+            <div class='searchformwrapper'>
+              <form action='../search.html' class='search' method='get'>
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+                <input type='submit' value='Go'/>
+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
 
 
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+
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-      
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
+
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-      <a href="../_sources/generated/modulation.entry_points.cli.rst.txt"
-          rel="nofollow">Page source</a>
+      <a href='../_sources/generated/modulation.entry_points.cli.rst.txt'
+         rel='nofollow'>Page source</a>
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-    
 
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\ No newline at end of file
diff --git a/docs/github/html/generated/modulation.entry_points.cli.main.html b/docs/github/html/generated/modulation.entry_points.cli.main.html
index 4ee809b..edba97c 100644
--- a/docs/github/html/generated/modulation.entry_points.cli.main.html
+++ b/docs/github/html/generated/modulation.entry_points.cli.main.html
@@ -1,154 +1,154 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+    <meta charset='utf-8'/>
+    <meta content='width=device-width, initial-scale=1.0' name='viewport'/>
+    <meta content='Docutils 0.17.1: http://docutils.sourceforge.net/' name='generator'/>
 
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-    <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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-  
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-        <div class="bodywrapper">
-          
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-          <div class="body" role="main">
-            
-  <section id="modulation-entry-points-cli-main">
-<h1>modulation.entry_points.cli.main<a class="headerlink" href="#modulation-entry-points-cli-main" title="Permalink to this heading">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="modulation.entry_points.cli.main">
-<span class="sig-prename descclassname"><span class="pre">modulation.entry_points.cli.</span></span><span class="sig-name descname"><span class="pre">main</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_draw_header</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/modulation/entry_points/cli.html#main"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#modulation.entry_points.cli.main" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>always_draw_header</strong> – </p>
-</dd>
-</dl>
-</dd></dl>
-
-</section>
+    <link href='../_static/pygments.css' rel='stylesheet' type='text/css'/>
+    <link href='../_static/alabaster.css' rel='stylesheet' type='text/css'/>
+    <link href='../_static/graphviz.css' rel='stylesheet' type='text/css'/>
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+              <h1>modulation.entry_points.cli.main<a class='headerlink' href='#modulation-entry-points-cli-main' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.entry_points.cli.main'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.entry_points.cli.</span></span><span class='sig-name descname'><span class='pre'>main</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='o'><span class='pre'>*</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>always_draw_header</span></span><span class='o'><span class='pre'>=</span></span><span class='default_value'><span class='pre'>False</span></span></em><span class='sig-paren'>)</span><a class='reference internal' href='../_modules/modulation/entry_points/cli.html#main'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.entry_points.cli.main' title='Permalink to this definition'>¶</a></dt>
+                <dd>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'><p><strong>always_draw_header</strong> – </p>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
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diff --git a/docs/github/html/generated/modulation.entry_points.html b/docs/github/html/generated/modulation.entry_points.html
index ba474a7..438480a 100644
--- a/docs/github/html/generated/modulation.entry_points.html
+++ b/docs/github/html/generated/modulation.entry_points.html
@@ -1,150 +1,146 @@
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-  <section id="module-modulation.entry_points">
-<span id="modulation-entry-points"></span><h1>modulation.entry_points<a class="headerlink" href="#module-modulation.entry_points" title="Permalink to this heading">¶</a></h1>
-<p class="rubric">Modules</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.entry_points.cli.html#module-modulation.entry_points.cli" title="modulation.entry_points.cli"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.entry_points.cli</span></code></a></p></td>
-<td><p>Created on 09/02/2020</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.entry_points.tensorboard_entry_point.html#module-modulation.entry_points.tensorboard_entry_point" title="modulation.entry_points.tensorboard_entry_point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.entry_points.tensorboard_entry_point</span></code></a></p></td>
-<td><p>description</p></td>
-</tr>
-</tbody>
-</table>
-</section>
+    <link href='../_static/pygments.css' rel='stylesheet' type='text/css'/>
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+            <section id='module-modulation.entry_points'>
+              <span id='modulation-entry-points'></span>
+              <h1>modulation.entry_points<a class='headerlink' href='#module-modulation.entry_points' title='Permalink to this heading'>¶</a></h1>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.entry_points.cli.html#module-modulation.entry_points.cli' title='modulation.entry_points.cli'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.entry_points.cli</span></code></a></p></td>
+                    <td><p>Created on 09/02/2020</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.entry_points.tensorboard_entry_point.html#module-modulation.entry_points.tensorboard_entry_point' title='modulation.entry_points.tensorboard_entry_point'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.entry_points.tensorboard_entry_point</span></code></a></p></td>
+                    <td><p>description</p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
 
 
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
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diff --git a/docs/github/html/generated/modulation.get_version.html b/docs/github/html/generated/modulation.get_version.html
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+++ b/docs/github/html/generated/modulation.get_version.html
@@ -1,147 +1,142 @@
-
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-  <section id="modulation-get-version">
-<h1>modulation.get_version<a class="headerlink" href="#modulation-get-version" title="Permalink to this heading">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="modulation.get_version">
-<span class="sig-prename descclassname"><span class="pre">modulation.</span></span><span class="sig-name descname"><span class="pre">get_version</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">append_time</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.10)"><span class="pre">Any</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><span class="pre">str</span></a></span></span><a class="reference internal" href="../_modules/modulation.html#get_version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#modulation.get_version" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>append_time</strong> – </p>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p></p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-</section>
+    <link href='../_static/pygments.css' rel='stylesheet' type='text/css'/>
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+            <section id='modulation-get-version'>
+              <h1>modulation.get_version<a class='headerlink' href='#modulation-get-version' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.get_version'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.</span></span><span class='sig-name descname'><span class='pre'>get_version</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>append_time</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Any' title='(in Python v3.10)'><span class='pre'>Any</span></a></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>True</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://docs.python.org/3/library/stdtypes.html#str' title='(in Python v3.10)'><span
+                    class='pre'>str</span></a></span></span><a class='reference internal' href='../_modules/modulation.html#get_version'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.get_version' title='Permalink to this definition'>¶</a></dt>
+                <dd>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'><p><strong>append_time</strong> – </p>
+                    </dd>
+                    <dt class='field-even'>Returns</dt>
+                    <dd class='field-even'><p></p>
+                    </dd>
+                    <dt class='field-odd'>Return type</dt>
+                    <dd class='field-odd'><p></p>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
+            </section>
 
 
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+                <li class='toctree-l2'><a class='reference internal' href='modulation.signal_utilities.html'>modulation.signal_utilities</a></li>
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+              </ul>
+            </li>
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+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
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+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
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-      <a href="../_sources/generated/modulation.get_version.rst.txt"
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diff --git a/docs/github/html/generated/modulation.html b/docs/github/html/generated/modulation.html
index 87086f6..6c8c874 100644
--- a/docs/github/html/generated/modulation.html
+++ b/docs/github/html/generated/modulation.html
@@ -1,180 +1,182 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
   <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+    <meta charset='utf-8'/>
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+    <meta content='Docutils 0.17.1: http://docutils.sourceforge.net/' name='generator'/>
 
     <title>modulation &#8212; modulation 0.0.1 documentation</title>
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-            
-  <section id="module-modulation">
-<span id="modulation"></span><h1>modulation<a class="headerlink" href="#module-modulation" title="Permalink to this heading">¶</a></h1>
-<span class="target" id="module-0"></span><p>Created on 27/04/2019</p>
-<p>&#64;author: cnheider</p>
-<p class="rubric">Functions</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.get_version.html#modulation.get_version" title="modulation.get_version"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_version</span></code></a>([append_time])</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param append_time</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-</tbody>
-</table>
-<p class="rubric">Modules</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.classification.html#module-modulation.classification" title="modulation.classification"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.classification</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.data.html#module-modulation.data" title="modulation.data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.data</span></code></a></p></td>
-<td><p>description</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.entry_points.html#module-modulation.entry_points" title="modulation.entry_points"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.entry_points</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.jax_utilities.html#module-modulation.jax_utilities" title="modulation.jax_utilities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.jax_utilities</span></code></a></p></td>
-<td><p>Created on 8/17/22</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.regression.html#module-modulation.regression" title="modulation.regression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.regression</span></code></a></p></td>
-<td><p>Created on 02-12-2020</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.signal_utilities.html#module-modulation.signal_utilities" title="modulation.signal_utilities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.signal_utilities</span></code></a></p></td>
-<td><p>Created on 7/8/22</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.torch_utilities.html#module-modulation.torch_utilities" title="modulation.torch_utilities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.torch_utilities</span></code></a></p></td>
-<td><p>Created on 17-12-2020</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.visualisation.html#module-modulation.visualisation" title="modulation.visualisation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.visualisation</span></code></a></p></td>
-<td><p>Created on 8/2/22</p></td>
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+            <section id='module-modulation'>
+              <span id='modulation'></span>
+              <h1>modulation<a class='headerlink' href='#module-modulation' title='Permalink to this heading'>¶</a></h1>
+              <span class='target' id='module-0'></span>
+              <p>Created on 27/04/2019</p>
+              <p>&#64;author: cnheider</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.get_version.html#modulation.get_version' title='modulation.get_version'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>get_version</span></code></a>([append_time])</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param append_time</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                </tbody>
+              </table>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.classification.html#module-modulation.classification' title='modulation.classification'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.classification</span></code></a></p></td>
+                    <td><p></p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.data.html#module-modulation.data' title='modulation.data'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.data</span></code></a></p></td>
+                    <td><p>description</p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.entry_points.html#module-modulation.entry_points' title='modulation.entry_points'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.entry_points</span></code></a></p></td>
+                    <td><p></p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.jax_utilities.html#module-modulation.jax_utilities' title='modulation.jax_utilities'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.jax_utilities</span></code></a></p></td>
+                    <td><p>Created on 8/17/22</p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.regression.html#module-modulation.regression' title='modulation.regression'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.regression</span></code></a></p></td>
+                    <td><p>Created on 02-12-2020</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.html#module-modulation.signal_utilities' title='modulation.signal_utilities'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.signal_utilities</span></code></a></p></td>
+                    <td><p>Created on 7/8/22</p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.torch_utilities.html#module-modulation.torch_utilities' title='modulation.torch_utilities'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.torch_utilities</span></code></a></p></td>
+                    <td><p>Created on 17-12-2020</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.visualisation.html#module-modulation.visualisation' title='modulation.visualisation'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.visualisation</span></code></a></p></td>
+                    <td><p>Created on 8/2/22</p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
 
 
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+                <li class='toctree-l2'><a class='reference internal' href='modulation.signal_utilities.html'>modulation.signal_utilities</a></li>
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+              </ul>
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+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
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+            <ul>
+              <li><a href='../index.html'>Documentation overview</a>
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diff --git a/docs/github/html/generated/modulation.jax_utilities.fourier.html b/docs/github/html/generated/modulation.jax_utilities.fourier.html
index be04536..0f0ca45 100644
--- a/docs/github/html/generated/modulation.jax_utilities.fourier.html
+++ b/docs/github/html/generated/modulation.jax_utilities.fourier.html
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-<p>Created on 8/17/22</p>
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+              <h1>modulation.jax_utilities.fourier<a class='headerlink' href='#module-modulation.jax_utilities.fourier' title='Permalink to this heading'>¶</a></h1>
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+              <h1>modulation.jax_utilities<a class='headerlink' href='#module-modulation.jax_utilities' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 8/17/22</p>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
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+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
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+                    <td><p><a class='reference internal' href='modulation.jax_utilities.fourier.html#module-modulation.jax_utilities.fourier' title='modulation.jax_utilities.fourier'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.jax_utilities.fourier</span></code></a></p></td>
+                    <td><p>Created on 8/17/22</p></td>
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diff --git a/docs/github/html/generated/modulation.noise_generation.additive_noise.compute_additive_noise_samples.html b/docs/github/html/generated/modulation.noise_generation.additive_noise.compute_additive_noise_samples.html
new file mode 100644
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+++ b/docs/github/html/generated/modulation.noise_generation.additive_noise.compute_additive_noise_samples.html
@@ -0,0 +1,124 @@
+<!DOCTYPE html>
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+            <section id='modulation-noise-generation-additive-noise-compute-additive-noise-samples'>
+              <h1>modulation.noise_generation.additive_noise.compute_additive_noise_samples<a class='headerlink' href='#modulation-noise-generation-additive-noise-compute-additive-noise-samples' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.noise_generation.additive_noise.compute_additive_noise_samples'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.noise_generation.additive_noise.</span></span><span class='sig-name descname'><span class='pre'>compute_additive_noise_samples</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='o'><span class='pre'>*</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>voice_activity_mask</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray' title='(in NumPy v1.23)'><span class='pre'>ndarray</span></a></span></em>, <em class='sig-param'><span class='n'><span class='pre'>signal_file</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/pathlib.html#pathlib.Path'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  title='(in Python v3.10)'><span class='pre'>Path</span></a></span></em>, <em class='sig-param'><span class='n'><span
+                    class='pre'>category</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>out_dir</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>noise_file</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>snrs</span></span><span class='o'><span class='pre'>=</span></span><span class='default_value'><span class='pre'>[0,</span> <span class='pre'>5,</span> <span class='pre'>10,</span> <span class='pre'>15,</span> <span class='pre'>20]</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>verbose</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#bool' title='(in Python v3.10)'><span class='pre'>bool</span></a></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span
+                    class='pre'>False</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://docs.python.org/3/library/constants.html#None' title='(in Python v3.10)'><span class='pre'>None</span></a></span></span><a class='reference internal' href='../_modules/modulation/noise_generation/additive_noise.html#compute_additive_noise_samples'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.noise_generation.additive_noise.compute_additive_noise_samples' title='Permalink to this definition'>¶</a></dt>
+                <dd>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'>
+                      <ul class='simple'>
+                        <li><p><strong>voice_activity_mask</strong> – </p></li>
+                        <li><p><strong>signal_file</strong> – </p></li>
+                        <li><p><strong>category</strong> – </p></li>
+                        <li><p><strong>out_dir</strong> – </p></li>
+                        <li><p><strong>noise_file</strong> – </p></li>
+                        <li><p><strong>snrs</strong> – </p></li>
+                        <li><p><strong>verbose</strong> – </p></li>
+                      </ul>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
+            </section>
+
+
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+
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+
+            <section id='module-modulation.noise_generation.additive_noise'>
+              <span id='modulation-noise-generation-additive-noise'></span>
+              <h1>modulation.noise_generation.additive_noise<a class='headerlink' href='#module-modulation.noise_generation.additive_noise' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 10-12-2020</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.additive_noise.compute_additive_noise_samples.html#modulation.noise_generation.additive_noise.compute_additive_noise_samples' title='modulation.noise_generation.additive_noise.compute_additive_noise_samples'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>compute_additive_noise_samples</span></code></a>(*, ...[, ...])</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param voice_activity_mask</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.additive_noise.sample_noise.html#modulation.noise_generation.additive_noise.sample_noise' title='modulation.noise_generation.additive_noise.sample_noise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>sample_noise</span></code></a>(noise, noise_rate, signal_len, ...)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param noise</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
+
+
+          </div>
+
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diff --git a/docs/github/html/generated/modulation.noise_generation.additive_noise.sample_noise.html b/docs/github/html/generated/modulation.noise_generation.additive_noise.sample_noise.html
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index 0000000..41d5fd6
--- /dev/null
+++ b/docs/github/html/generated/modulation.noise_generation.additive_noise.sample_noise.html
@@ -0,0 +1,127 @@
+<!DOCTYPE html>
+
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+
+            <section id='modulation-noise-generation-additive-noise-sample-noise'>
+              <h1>modulation.noise_generation.additive_noise.sample_noise<a class='headerlink' href='#modulation-noise-generation-additive-noise-sample-noise' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.noise_generation.additive_noise.sample_noise'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.noise_generation.additive_noise.</span></span><span class='sig-name descname'><span class='pre'>sample_noise</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>noise</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>noise_rate</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#int' title='(in Python v3.10)'><span class='pre'>int</span></a></span></em>, <em class='sig-param'><span class='n'><span class='pre'>signal_len</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#int' title='(in Python v3.10)'><span class='pre'>int</span></a></span></em>, <em
+                    class='sig-param'><span class='n'><span class='pre'>signal_rate</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#int' title='(in Python v3.10)'><span class='pre'>int</span></a></span></em>, <em class='sig-param'><span class='o'><span class='pre'>*</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>resample_noise</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#bool' title='(in Python v3.10)'><span class='pre'>bool</span></a></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>True</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span
+                    class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Any' title='(in Python v3.10)'><span class='pre'>Any</span></a></span></span><a class='reference internal' href='../_modules/modulation/noise_generation/additive_noise.html#sample_noise'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.noise_generation.additive_noise.sample_noise' title='Permalink to this definition'>¶</a></dt>
+                <dd>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'>
+                      <ul class='simple'>
+                        <li><p><strong>noise</strong> – </p></li>
+                        <li><p><strong>noise_rate</strong> – </p></li>
+                        <li><p><strong>signal_len</strong> – </p></li>
+                        <li><p><strong>signal_rate</strong> – </p></li>
+                        <li><p><strong>resample_noise</strong> – </p></li>
+                      </ul>
+                    </dd>
+                    <dt class='field-even'>Returns</dt>
+                    <dd class='field-even'><p></p>
+                    </dd>
+                    <dt class='field-odd'>Return type</dt>
+                    <dd class='field-odd'><p></p>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
+            </section>
+
+
+          </div>
+
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+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
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diff --git a/docs/github/html/generated/modulation.noise_generation.babble_noise.generate_babble_noise.html b/docs/github/html/generated/modulation.noise_generation.babble_noise.generate_babble_noise.html
new file mode 100644
index 0000000..e5fde6f
--- /dev/null
+++ b/docs/github/html/generated/modulation.noise_generation.babble_noise.generate_babble_noise.html
@@ -0,0 +1,127 @@
+<!DOCTYPE html>
+
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+
+            <section id='modulation-noise-generation-babble-noise-generate-babble-noise'>
+              <h1>modulation.noise_generation.babble_noise.generate_babble_noise<a class='headerlink' href='#modulation-noise-generation-babble-noise-generate-babble-noise' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.noise_generation.babble_noise.generate_babble_noise'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.noise_generation.babble_noise.</span></span><span class='sig-name descname'><span class='pre'>generate_babble_noise</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>samples</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Iterable' title='(in Python v3.10)'><span class='pre'>Iterable</span></a><span class='p'><span class='pre'>[</span></span><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Iterable' title='(in Python v3.10)'><span class='pre'>Iterable</span></a><span class='p'><span class='pre'>[</span></span><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Sequence' title='(in Python v3.10)'><span
+                    class='pre'>Sequence</span></a><span class='p'><span class='pre'>]</span></span><span class='p'><span class='pre'>]</span></span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>sampling_rate</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#int' title='(in Python v3.10)'><span class='pre'>int</span></a></span></em>, <em class='sig-param'><span class='o'><span class='pre'>*</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>export_path</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Optional' title='(in Python v3.10)'><span class='pre'>Optional</span></a><span class='p'><span class='pre'>[</span></span><a class='reference external'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               href='https://docs.python.org/3/library/pathlib.html#pathlib.Path'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               title='(in Python v3.10)'><span
+                    class='pre'>Path</span></a><span class='p'><span class='pre'>]</span></span></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>None</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Iterable' title='(in Python v3.10)'><span class='pre'>Iterable</span></a></span></span><a class='reference internal' href='../_modules/modulation/noise_generation/babble_noise.html#generate_babble_noise'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.noise_generation.babble_noise.generate_babble_noise' title='Permalink to this definition'>¶</a></dt>
+                <dd>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'>
+                      <ul class='simple'>
+                        <li><p><strong>samples</strong> – </p></li>
+                        <li><p><strong>sampling_rate</strong> – </p></li>
+                        <li><p><strong>export_path</strong> – </p></li>
+                      </ul>
+                    </dd>
+                    <dt class='field-even'>Returns</dt>
+                    <dd class='field-even'><p></p>
+                    </dd>
+                    <dt class='field-odd'>Return type</dt>
+                    <dd class='field-odd'><p></p>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
+            </section>
+
+
+          </div>
+
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diff --git a/docs/github/html/generated/modulation.noise_generation.babble_noise.html b/docs/github/html/generated/modulation.noise_generation.babble_noise.html
new file mode 100644
index 0000000..77dba2b
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+++ b/docs/github/html/generated/modulation.noise_generation.babble_noise.html
@@ -0,0 +1,120 @@
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+
+            <section id='module-modulation.noise_generation.babble_noise'>
+              <span id='modulation-noise-generation-babble-noise'></span>
+              <h1>modulation.noise_generation.babble_noise<a class='headerlink' href='#module-modulation.noise_generation.babble_noise' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 09-12-2020</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.babble_noise.generate_babble_noise.html#modulation.noise_generation.babble_noise.generate_babble_noise' title='modulation.noise_generation.babble_noise.generate_babble_noise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>generate_babble_noise</span></code></a>(samples, sampling_rate, *)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param samples</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
+
+
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+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
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diff --git a/docs/github/html/generated/modulation.noise_generation.deprecated.html b/docs/github/html/generated/modulation.noise_generation.deprecated.html
new file mode 100644
index 0000000..aa809aa
--- /dev/null
+++ b/docs/github/html/generated/modulation.noise_generation.deprecated.html
@@ -0,0 +1,111 @@
+<!DOCTYPE html>
+
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+
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+
+            <section id='module-modulation.noise_generation.deprecated'>
+              <span id='modulation-noise-generation-deprecated'></span>
+              <h1>modulation.noise_generation.deprecated<a class='headerlink' href='#module-modulation.noise_generation.deprecated' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 30-12-2020</p>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                </tbody>
+              </table>
+            </section>
+
+
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+
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+      </div>
+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
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+            <ul>
+              <li><a href='../index.html'>Documentation overview</a>
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diff --git a/docs/github/html/generated/modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.html b/docs/github/html/generated/modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.html
new file mode 100644
index 0000000..bcb33aa
--- /dev/null
+++ b/docs/github/html/generated/modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.html
@@ -0,0 +1,143 @@
+<!DOCTYPE html>
+
+<html lang='en'>
+  <head>
+    <meta charset='utf-8'/>
+    <meta name='viewport' content='width=device-width, initial-scale=1.0'/>
+    <meta name='generator' content='Docutils 0.17.1: http://docutils.sourceforge.net/'/>
+
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+    <link rel='stylesheet' type='text/css' href='../_static/pygments.css'/>
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+
+            <section id='modulation-noise-generation-gaussian-noise-gaussiannoisetypeenum'>
+              <h1>modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum<a class='headerlink' href='#modulation-noise-generation-gaussian-noise-gaussiannoisetypeenum' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py class'>
+                <dt class='sig sig-object py' id='modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum'>
+                  <em class='property'><span class='pre'>class</span><span class='w'> </span></em><span class='sig-prename descclassname'><span class='pre'>modulation.noise_generation.gaussian_noise.</span></span><span class='sig-name descname'><span class='pre'>GaussianNoiseTypeEnum</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>value</span></span></em><span class='sig-paren'>)</span><a class='reference internal' href='../_modules/modulation/noise_generation/gaussian_noise.html#GaussianNoiseTypeEnum'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum' title='Permalink to this definition'>¶</a></dt>
+                <dd><p>Bases: <a class='reference external' href='https://docs.python.org/3/library/enum.html#enum.Enum' title='(in Python v3.10)'><code class='xref py py-class docutils literal notranslate'><span class='pre'>Enum</span></code></a></p>
+                  <p>description</p>
+                  <dl class='py method'>
+                    <dt class='sig sig-object py' id='modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.__init__'>
+                      <span class='sig-name descname'><span class='pre'>__init__</span></span><span class='sig-paren'>(</span><span class='sig-paren'>)</span><a class='headerlink' href='#modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.__init__' title='Permalink to this definition'>¶</a></dt>
+                    <dd></dd>
+                  </dl>
+
+                  <p class='rubric'>Attributes</p>
+                  <table class='autosummary longtable docutils align-default'>
+                    <colgroup>
+                      <col style='width: 10%'/>
+                      <col style='width: 90%'/>
+                    </colgroup>
+                    <tbody>
+                      <tr class='row-odd'>
+                        <td><p><code class='xref py py-obj docutils literal notranslate'><span class='pre'>white</span></code></p></td>
+                        <td><p></p></td>
+                      </tr>
+                      <tr class='row-even'>
+                        <td><p><code class='xref py py-obj docutils literal notranslate'><span class='pre'>brown</span></code></p></td>
+                        <td><p></p></td>
+                      </tr>
+                      <tr class='row-odd'>
+                        <td><p><code class='xref py py-obj docutils literal notranslate'><span class='pre'>blue</span></code></p></td>
+                        <td><p></p></td>
+                      </tr>
+                      <tr class='row-even'>
+                        <td><p><code class='xref py py-obj docutils literal notranslate'><span class='pre'>pink</span></code></p></td>
+                        <td><p></p></td>
+                      </tr>
+                      <tr class='row-odd'>
+                        <td><p><code class='xref py py-obj docutils literal notranslate'><span class='pre'>violet</span></code></p></td>
+                        <td><p></p></td>
+                      </tr>
+                    </tbody>
+                  </table>
+                </dd>
+              </dl>
+
+            </section>
+
+
+          </div>
+
+        </div>
+      </div>
+      <div class='sphinxsidebar' role='navigation' aria-label='main navigation'>
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../index.html'>Documentation overview</a>
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diff --git a/docs/github/html/generated/modulation.noise_generation.gaussian_noise.blue_noise.html b/docs/github/html/generated/modulation.noise_generation.gaussian_noise.blue_noise.html
new file mode 100644
index 0000000..c4605f7
--- /dev/null
+++ b/docs/github/html/generated/modulation.noise_generation.gaussian_noise.blue_noise.html
@@ -0,0 +1,115 @@
+<!DOCTYPE html>
+
+<html lang='en'>
+  <head>
+    <meta charset='utf-8'/>
+    <meta name='viewport' content='width=device-width, initial-scale=1.0'/>
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+
+    <title>modulation.noise_generation.gaussian_noise.blue_noise &#8212; modulation 0.0.1 documentation</title>
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+
+            <section id='modulation-noise-generation-gaussian-noise-blue-noise'>
+              <h1>modulation.noise_generation.gaussian_noise.blue_noise<a class='headerlink' href='#modulation-noise-generation-gaussian-noise-blue-noise' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.noise_generation.gaussian_noise.blue_noise'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.noise_generation.gaussian_noise.</span></span><span class='sig-name descname'><span class='pre'>blue_noise</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>length</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#int' title='(in Python v3.10)'><span class='pre'>int</span></a></span></em>, <em class='sig-param'><span class='n'><span class='pre'>seed</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Optional' title='(in Python v3.10)'><span class='pre'>Optional</span></a><span class='p'><span class='pre'>[</span></span><a class='reference external'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               href='https://docs.python.org/3/library/functions.html#int'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               title='(in Python v3.10)'><span
+                    class='pre'>int</span></a><span class='p'><span class='pre'>]</span></span></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>None</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray' title='(in NumPy v1.23)'><span class='pre'>ndarray</span></a></span></span><a class='reference internal' href='../_modules/modulation/noise_generation/gaussian_noise.html#blue_noise'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.noise_generation.gaussian_noise.blue_noise' title='Permalink to this definition'>¶</a></dt>
+                <dd><p>Blue noise.</p>
+                  <ul class='simple'>
+                    <li><p>N: Amount of samples.</p></li>
+                  </ul>
+                  <p>Power increases with 6 dB per octave.
+                     Power density increases with 3 dB per octave.</p>
+                </dd>
+              </dl>
+
+            </section>
+
+
+          </div>
+
+        </div>
+      </div>
+      <div class='sphinxsidebar' role='navigation' aria-label='main navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <h1 class='logo'><a href='../index.html'>modulation</a></h1>
+
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='modulation.html'>modulation</a></li>
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
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+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../index.html'>Documentation overview</a>
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diff --git a/docs/github/html/generated/modulation.noise_generation.gaussian_noise.brown_noise.html b/docs/github/html/generated/modulation.noise_generation.gaussian_noise.brown_noise.html
new file mode 100644
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+            <section id='modulation-noise-generation-gaussian-noise-brown-noise'>
+              <h1>modulation.noise_generation.gaussian_noise.brown_noise<a class='headerlink' href='#modulation-noise-generation-gaussian-noise-brown-noise' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.noise_generation.gaussian_noise.brown_noise'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.noise_generation.gaussian_noise.</span></span><span class='sig-name descname'><span class='pre'>brown_noise</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>length</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#int' title='(in Python v3.10)'><span class='pre'>int</span></a></span></em>, <em class='sig-param'><span class='n'><span class='pre'>seed</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Optional' title='(in Python v3.10)'><span class='pre'>Optional</span></a><span class='p'><span class='pre'>[</span></span><a class='reference external'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                href='https://docs.python.org/3/library/functions.html#int'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                title='(in Python v3.10)'><span class='pre'>int</span></a><span
+                    class='p'><span class='pre'>]</span></span></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>None</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray' title='(in NumPy v1.23)'><span class='pre'>ndarray</span></a></span></span><a class='reference internal' href='../_modules/modulation/noise_generation/gaussian_noise.html#brown_noise'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.noise_generation.gaussian_noise.brown_noise' title='Permalink to this definition'>¶</a></dt>
+                <dd><p>Brown noise.</p>
+                  <ul class='simple'>
+                    <li><p>N: Amount of samples.</p></li>
+                  </ul>
+                  <p>Power decreases with -3 dB per octave.
+                     Power density decreases with 6 dB per octave.</p>
+                </dd>
+              </dl>
+
+            </section>
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diff --git a/docs/github/html/generated/modulation.noise_generation.gaussian_noise.generate_noise.html b/docs/github/html/generated/modulation.noise_generation.gaussian_noise.generate_noise.html
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+            <section id='modulation-noise-generation-gaussian-noise-generate-noise'>
+              <h1>modulation.noise_generation.gaussian_noise.generate_noise<a class='headerlink' href='#modulation-noise-generation-gaussian-noise-generate-noise' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.noise_generation.gaussian_noise.generate_noise'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.noise_generation.gaussian_noise.</span></span><span class='sig-name descname'><span class='pre'>generate_noise</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>length</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#int' title='(in Python v3.10)'><span class='pre'>int</span></a></span></em>, <em class='sig-param'><span class='o'><span class='pre'>*</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>seed</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Optional' title='(in Python v3.10)'><span class='pre'>Optional</span></a><span class='p'><span
+                    class='pre'>[</span></span><a class='reference external' href='https://docs.python.org/3/library/functions.html#int' title='(in Python v3.10)'><span class='pre'>int</span></a><span class='p'><span class='pre'>]</span></span></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>None</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>noise_type</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference internal' href='modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.html#modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum' title='modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum'><span class='pre'>GaussianNoiseTypeEnum</span></a></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span
+                    class='default_value'><span class='pre'>GaussianNoiseTypeEnum.white</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>export_path</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Optional' title='(in Python v3.10)'><span class='pre'>Optional</span></a><span class='p'><span class='pre'>[</span></span><a class='reference external' href='https://docs.python.org/3/library/pathlib.html#pathlib.Path' title='(in Python v3.10)'><span class='pre'>Path</span></a><span class='p'><span class='pre'>]</span></span></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>None</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>sampling_rate</span></span><span class='p'><span
+                    class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#int' title='(in Python v3.10)'><span class='pre'>int</span></a></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>16000</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray' title='(in NumPy v1.23)'><span class='pre'>ndarray</span></a></span></span><a class='reference internal' href='../_modules/modulation/noise_generation/gaussian_noise.html#generate_noise'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                href='#modulation.noise_generation.gaussian_noise.generate_noise'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                title='Permalink to this definition'>¶</a></dt>
+                <dd>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'>
+                      <ul class='simple'>
+                        <li><p><strong>length</strong> – </p></li>
+                        <li><p><strong>seed</strong> – </p></li>
+                        <li><p><strong>noise_type</strong> – </p></li>
+                        <li><p><strong>export_path</strong> – </p></li>
+                        <li><p><strong>sampling_rate</strong> – </p></li>
+                      </ul>
+                    </dd>
+                    <dt class='field-even'>Returns</dt>
+                    <dd class='field-even'><p></p>
+                    </dd>
+                    <dt class='field-odd'>Return type</dt>
+                    <dd class='field-odd'><p></p>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
+            </section>
+
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new file mode 100644
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+            <section id='module-modulation.noise_generation.gaussian_noise'>
+              <span id='modulation-noise-generation-gaussian-noise'></span>
+              <h1>modulation.noise_generation.gaussian_noise<a class='headerlink' href='#module-modulation.noise_generation.gaussian_noise' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 10-12-2020</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.gaussian_noise.blue_noise.html#modulation.noise_generation.gaussian_noise.blue_noise' title='modulation.noise_generation.gaussian_noise.blue_noise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>blue_noise</span></code></a>(length[, seed])</p></td>
+                    <td><p>Blue noise.</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.gaussian_noise.brown_noise.html#modulation.noise_generation.gaussian_noise.brown_noise' title='modulation.noise_generation.gaussian_noise.brown_noise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>brown_noise</span></code></a>(length[, seed])</p></td>
+                    <td><p>Brown noise.</p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.gaussian_noise.generate_noise.html#modulation.noise_generation.gaussian_noise.generate_noise' title='modulation.noise_generation.gaussian_noise.generate_noise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>generate_noise</span></code></a>(length, *[, seed, ...])</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param length</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.gaussian_noise.pink_noise.html#modulation.noise_generation.gaussian_noise.pink_noise' title='modulation.noise_generation.gaussian_noise.pink_noise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>pink_noise</span></code></a>(length[, seed])</p></td>
+                    <td><p>Pink noise.</p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.gaussian_noise.violet_noise.html#modulation.noise_generation.gaussian_noise.violet_noise' title='modulation.noise_generation.gaussian_noise.violet_noise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>violet_noise</span></code></a>(length[, seed])</p></td>
+                    <td><p>Violet noise.</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.gaussian_noise.white_noise.html#modulation.noise_generation.gaussian_noise.white_noise' title='modulation.noise_generation.gaussian_noise.white_noise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>white_noise</span></code></a>(length[, seed])</p></td>
+                    <td><p>White noise.</p></td>
+                  </tr>
+                </tbody>
+              </table>
+              <p class='rubric'>Classes</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.html#modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum' title='modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>GaussianNoiseTypeEnum</span></code></a>(value)</p></td>
+                    <td><p>description</p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
+
+
+          </div>
+
+        </div>
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diff --git a/docs/github/html/generated/modulation.noise_generation.gaussian_noise.pink_noise.html b/docs/github/html/generated/modulation.noise_generation.gaussian_noise.pink_noise.html
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+            <section id='modulation-noise-generation-gaussian-noise-pink-noise'>
+              <h1>modulation.noise_generation.gaussian_noise.pink_noise<a class='headerlink' href='#modulation-noise-generation-gaussian-noise-pink-noise' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.noise_generation.gaussian_noise.pink_noise'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.noise_generation.gaussian_noise.</span></span><span class='sig-name descname'><span class='pre'>pink_noise</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>length</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#int' title='(in Python v3.10)'><span class='pre'>int</span></a></span></em>, <em class='sig-param'><span class='n'><span class='pre'>seed</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Optional' title='(in Python v3.10)'><span class='pre'>Optional</span></a><span class='p'><span class='pre'>[</span></span><a class='reference external'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               href='https://docs.python.org/3/library/functions.html#int'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               title='(in Python v3.10)'><span
+                    class='pre'>int</span></a><span class='p'><span class='pre'>]</span></span></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>None</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray' title='(in NumPy v1.23)'><span class='pre'>ndarray</span></a></span></span><a class='reference internal' href='../_modules/modulation/noise_generation/gaussian_noise.html#pink_noise'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.noise_generation.gaussian_noise.pink_noise' title='Permalink to this definition'>¶</a></dt>
+                <dd><p>Pink noise.</p>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'>
+                      <ul class='simple'>
+                        <li><p><strong>length</strong> – Amount of samples.</p></li>
+                        <li><p><strong>seed</strong> (<a class='reference external' href='https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState' title='(in NumPy v1.23)'><code class='xref py py-class docutils literal notranslate'><span class='pre'>numpy.random.RandomState</span></code></a>) – State of PRNG.</p></li>
+                      </ul>
+                    </dd>
+                  </dl>
+                  <p>Pink noise has equal power in bands that are proportionally wide.
+                     Power density decreases with 3 dB per octave.</p>
+                </dd>
+              </dl>
+
+            </section>
+
+
+          </div>
+
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diff --git a/docs/github/html/generated/modulation.noise_generation.gaussian_noise.violet_noise.html b/docs/github/html/generated/modulation.noise_generation.gaussian_noise.violet_noise.html
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@@ -0,0 +1,121 @@
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+            <section id='modulation-noise-generation-gaussian-noise-violet-noise'>
+              <h1>modulation.noise_generation.gaussian_noise.violet_noise<a class='headerlink' href='#modulation-noise-generation-gaussian-noise-violet-noise' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.noise_generation.gaussian_noise.violet_noise'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.noise_generation.gaussian_noise.</span></span><span class='sig-name descname'><span class='pre'>violet_noise</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>length</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#int' title='(in Python v3.10)'><span class='pre'>int</span></a></span></em>, <em class='sig-param'><span class='n'><span class='pre'>seed</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Optional' title='(in Python v3.10)'><span class='pre'>Optional</span></a><span class='p'><span class='pre'>[</span></span><a class='reference external'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 href='https://docs.python.org/3/library/functions.html#int'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 title='(in Python v3.10)'><span class='pre'>int</span></a><span
+                    class='p'><span class='pre'>]</span></span></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>None</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray' title='(in NumPy v1.23)'><span class='pre'>ndarray</span></a></span></span><a class='reference internal' href='../_modules/modulation/noise_generation/gaussian_noise.html#violet_noise'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.noise_generation.gaussian_noise.violet_noise' title='Permalink to this definition'>¶</a></dt>
+                <dd><p>Violet noise. Power increases with 6 dB per octave.</p>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'>
+                      <ul class='simple'>
+                        <li><p><strong>length</strong> – Amount of samples.</p></li>
+                        <li><p><strong>seed</strong> (<a class='reference external' href='https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState' title='(in NumPy v1.23)'><code class='xref py py-class docutils literal notranslate'><span class='pre'>numpy.random.RandomState</span></code></a>) – State of PRNG.</p></li>
+                      </ul>
+                    </dd>
+                  </dl>
+                  <p>Power increases with +9 dB per octave.
+                     Power density increases with +6 dB per octave.</p>
+                </dd>
+              </dl>
+
+            </section>
+
+
+          </div>
+
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+            <section id='modulation-noise-generation-gaussian-noise-white-noise'>
+              <h1>modulation.noise_generation.gaussian_noise.white_noise<a class='headerlink' href='#modulation-noise-generation-gaussian-noise-white-noise' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.noise_generation.gaussian_noise.white_noise'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.noise_generation.gaussian_noise.</span></span><span class='sig-name descname'><span class='pre'>white_noise</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>length</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/functions.html#int' title='(in Python v3.10)'><span class='pre'>int</span></a></span></em>, <em class='sig-param'><span class='n'><span class='pre'>seed</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Optional' title='(in Python v3.10)'><span class='pre'>Optional</span></a><span class='p'><span class='pre'>[</span></span><a class='reference external'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                href='https://docs.python.org/3/library/functions.html#int'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                title='(in Python v3.10)'><span class='pre'>int</span></a><span
+                    class='p'><span class='pre'>]</span></span></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>None</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray' title='(in NumPy v1.23)'><span class='pre'>ndarray</span></a></span></span><a class='reference internal' href='../_modules/modulation/noise_generation/gaussian_noise.html#white_noise'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.noise_generation.gaussian_noise.white_noise' title='Permalink to this definition'>¶</a></dt>
+                <dd><p>White noise.</p>
+                  <ul class='simple'>
+                    <li><p>N: Amount of samples.</p></li>
+                  </ul>
+                  <p>White noise has a constant power density.
+                     Its narrowband spectrum is therefore flat.
+                     The power in white noise will increase by a factor of two for each octave band,
+                     and therefore increases with 3 dB per octave.</p>
+                </dd>
+              </dl>
+
+            </section>
+
+
+          </div>
+
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+      </div>
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+            <section id='module-modulation.noise_generation'>
+              <span id='modulation-noise-generation'></span>
+              <h1>modulation.noise_generation<a class='headerlink' href='#module-modulation.noise_generation' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 09-12-2020</p>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.additive_noise.html#module-modulation.noise_generation.additive_noise' title='modulation.noise_generation.additive_noise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.noise_generation.additive_noise</span></code></a></p></td>
+                    <td><p>Created on 10-12-2020</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.babble_noise.html#module-modulation.noise_generation.babble_noise' title='modulation.noise_generation.babble_noise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.noise_generation.babble_noise</span></code></a></p></td>
+                    <td><p>Created on 09-12-2020</p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.deprecated.html#module-modulation.noise_generation.deprecated' title='modulation.noise_generation.deprecated'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.noise_generation.deprecated</span></code></a></p></td>
+                    <td><p>Created on 30-12-2020</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.gaussian_noise.html#module-modulation.noise_generation.gaussian_noise' title='modulation.noise_generation.gaussian_noise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.noise_generation.gaussian_noise</span></code></a></p></td>
+                    <td><p>Created on 10-12-2020</p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.shaped_noise.html#module-modulation.noise_generation.shaped_noise' title='modulation.noise_generation.shaped_noise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.noise_generation.shaped_noise</span></code></a></p></td>
+                    <td><p>Created on 09-12-2020</p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
+
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diff --git a/docs/github/html/generated/modulation.noise_generation.shaped_noise.generate_speech_shaped_noise.html b/docs/github/html/generated/modulation.noise_generation.shaped_noise.generate_speech_shaped_noise.html
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@@ -0,0 +1,112 @@
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+            <section id='modulation-noise-generation-shaped-noise-generate-speech-shaped-noise'>
+              <h1>modulation.noise_generation.shaped_noise.generate_speech_shaped_noise<a class='headerlink' href='#modulation-noise-generation-shaped-noise-generate-speech-shaped-noise' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.noise_generation.shaped_noise.generate_speech_shaped_noise'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.noise_generation.shaped_noise.</span></span><span class='sig-name descname'><span class='pre'>generate_speech_shaped_noise</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>samples</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Iterable' title='(in Python v3.10)'><span class='pre'>Iterable</span></a><span class='p'><span class='pre'>[</span></span><a class='reference external' href='https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray' title='(in NumPy v1.23)'><span class='pre'>ndarray</span></a><span class='p'><span class='pre'>]</span></span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>sampling_rate</span></span></em>, <em class='sig-param'><span
+                    class='o'><span class='pre'>*</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>export_path</span></span><span class='p'><span class='pre'>:</span></span><span class='w'> </span><span class='n'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Optional' title='(in Python v3.10)'><span class='pre'>Optional</span></a><span class='p'><span class='pre'>[</span></span><a class='reference external' href='https://docs.python.org/3/library/pathlib.html#pathlib.Path' title='(in Python v3.10)'><span class='pre'>Path</span></a><span class='p'><span class='pre'>]</span></span></span><span class='w'> </span><span class='o'><span class='pre'>=</span></span><span class='w'> </span><span class='default_value'><span class='pre'>None</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>long_term_avg</span></span><span class='o'><span class='pre'>=</span></span><span class='default_value'><span
+                    class='pre'>True</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>window_length_sec</span></span><span class='o'><span class='pre'>=</span></span><span class='default_value'><span class='pre'>0.02</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>p_overlap</span></span><span class='o'><span class='pre'>=</span></span><span class='default_value'><span class='pre'>0.5</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external' href='https://docs.python.org/3/library/typing.html#typing.Iterable' title='(in Python v3.10)'><span class='pre'>Iterable</span></a></span></span><a class='reference internal' href='../_modules/modulation/noise_generation/shaped_noise.html#generate_speech_shaped_noise'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             href='#modulation.noise_generation.shaped_noise.generate_speech_shaped_noise'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             title='Permalink to this definition'>¶</a>
+                </dt>
+                <dd><p>White (flat-spectrum) noise has more energy at high frequencies, and therefore produces more masking of the higher formants and frication noises than speech-shaped noise, speech shaped noise mask evenly throughout the speech signal spectrum</p>
+                </dd>
+              </dl>
+
+            </section>
+
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+
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diff --git a/docs/github/html/generated/modulation.noise_generation.shaped_noise.html b/docs/github/html/generated/modulation.noise_generation.shaped_noise.html
new file mode 100644
index 0000000..4b7f265
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@@ -0,0 +1,115 @@
+<!DOCTYPE html>
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+
+            <section id='module-modulation.noise_generation.shaped_noise'>
+              <span id='modulation-noise-generation-shaped-noise'></span>
+              <h1>modulation.noise_generation.shaped_noise<a class='headerlink' href='#module-modulation.noise_generation.shaped_noise' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 09-12-2020</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.noise_generation.shaped_noise.generate_speech_shaped_noise.html#modulation.noise_generation.shaped_noise.generate_speech_shaped_noise' title='modulation.noise_generation.shaped_noise.generate_speech_shaped_noise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>generate_speech_shaped_noise</span></code></a>(samples, ...[, ...])</p></td>
+                    <td><p>White (flat-spectrum) noise has more energy at high frequencies, and therefore produces more masking of the higher formants and frication noises than speech-shaped noise, speech shaped noise mask evenly throughout the speech signal spectrum</p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
+
+
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+
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+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
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+
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diff --git a/docs/github/html/generated/modulation.regression.denoising.band_pass.html b/docs/github/html/generated/modulation.regression.denoising.band_pass.html
index f4cc54e..2c2eb67 100644
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+++ b/docs/github/html/generated/modulation.regression.denoising.band_pass.html
@@ -1,145 +1,144 @@
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-<p>Created on 02-12-2020</p>
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-<span id="modulation-regression-denoising"></span><h1>modulation.regression.denoising<a class="headerlink" href="#module-modulation.regression.denoising" title="Permalink to this heading">¶</a></h1>
-<p>Created on 02-12-2020</p>
-<p class="rubric">Modules</p>
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+              <h1>modulation.regression.denoising<a class='headerlink' href='#module-modulation.regression.denoising' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 02-12-2020</p>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
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+                    <td><p><a class='reference internal' href='modulation.regression.denoising.band_pass.html#module-modulation.regression.denoising.band_pass' title='modulation.regression.denoising.band_pass'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.regression.denoising.band_pass</span></code></a></p></td>
+                    <td><p>Created on 02-12-2020</p></td>
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+                    <li class='toctree-l3'><a class='reference internal' href='modulation.regression.spectral_denoise.html'>modulation.regression.spectral_denoise</a></li>
+                  </ul>
+                </li>
+                <li class='toctree-l2'><a class='reference internal' href='modulation.signal_utilities.html'>modulation.signal_utilities</a></li>
+                <li class='toctree-l2'><a class='reference internal' href='modulation.torch_utilities.html'>modulation.torch_utilities</a></li>
+                <li class='toctree-l2'><a class='reference internal' href='modulation.visualisation.html'>modulation.visualisation</a></li>
+              </ul>
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+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
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diff --git a/docs/github/html/generated/modulation.regression.html b/docs/github/html/generated/modulation.regression.html
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-            
-  <section id="module-modulation.regression">
-<span id="modulation-regression"></span><h1>modulation.regression<a class="headerlink" href="#module-modulation.regression" title="Permalink to this heading">¶</a></h1>
-<p>Created on 02-12-2020</p>
-<p class="rubric">Modules</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.regression.denoising.html#module-modulation.regression.denoising" title="modulation.regression.denoising"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.regression.denoising</span></code></a></p></td>
-<td><p>Created on 02-12-2020</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.regression.linear_predictive_coding.html#module-modulation.regression.linear_predictive_coding" title="modulation.regression.linear_predictive_coding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.regression.linear_predictive_coding</span></code></a></p></td>
-<td><p>Created on 24-02-2021</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.regression.spectral_denoise.html#module-modulation.regression.spectral_denoise" title="modulation.regression.spectral_denoise"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.regression.spectral_denoise</span></code></a></p></td>
-<td><p>Created on 09/04/2020</p></td>
-</tr>
-</tbody>
-</table>
-</section>
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+              <span id='modulation-regression'></span>
+              <h1>modulation.regression<a class='headerlink' href='#module-modulation.regression' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 02-12-2020</p>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.regression.denoising.html#module-modulation.regression.denoising' title='modulation.regression.denoising'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.regression.denoising</span></code></a></p></td>
+                    <td><p>Created on 02-12-2020</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.regression.linear_predictive_coding.html#module-modulation.regression.linear_predictive_coding' title='modulation.regression.linear_predictive_coding'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.regression.linear_predictive_coding</span></code></a></p></td>
+                    <td><p>Created on 24-02-2021</p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.regression.spectral_denoise.html#module-modulation.regression.spectral_denoise' title='modulation.regression.spectral_denoise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.regression.spectral_denoise</span></code></a></p></td>
+                    <td><p>Created on 09/04/2020</p></td>
+                  </tr>
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diff --git a/docs/github/html/generated/modulation.regression.spectral_denoise.html b/docs/github/html/generated/modulation.regression.spectral_denoise.html
index f4b9a2d..4b4a34b 100644
--- a/docs/github/html/generated/modulation.regression.spectral_denoise.html
+++ b/docs/github/html/generated/modulation.regression.spectral_denoise.html
@@ -1,154 +1,152 @@
-
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-  <section id="module-modulation.regression.spectral_denoise">
-<span id="modulation-regression-spectral-denoise"></span><h1>modulation.regression.spectral_denoise<a class="headerlink" href="#module-modulation.regression.spectral_denoise" title="Permalink to this heading">¶</a></h1>
-<p>Created on 09/04/2020</p>
-<p class="rubric">Functions</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
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-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.regression.spectral_denoise.fft_denoise.html#modulation.regression.spectral_denoise.fft_denoise" title="modulation.regression.spectral_denoise.fft_denoise"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fft_denoise</span></code></a>(noisy_signal, num_time_steps[, ...])</p></td>
-<td><p>description</p></td>
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-</table>
-</section>
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+
+            <section id='module-modulation.regression.spectral_denoise'>
+              <span id='modulation-regression-spectral-denoise'></span>
+              <h1>modulation.regression.spectral_denoise<a class='headerlink' href='#module-modulation.regression.spectral_denoise' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 09/04/2020</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.regression.spectral_denoise.fft_denoise.html#modulation.regression.spectral_denoise.fft_denoise' title='modulation.regression.spectral_denoise.fft_denoise'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>fft_denoise</span></code></a>(noisy_signal, num_time_steps[, ...])</p></td>
+                    <td><p>description</p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
 
 
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diff --git a/docs/github/html/generated/modulation.salience.colorise_waveform.color_plot.html b/docs/github/html/generated/modulation.salience.colorise_waveform.color_plot.html
new file mode 100644
index 0000000..8291e0e
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+++ b/docs/github/html/generated/modulation.salience.colorise_waveform.color_plot.html
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+<!DOCTYPE html>
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+            <section id='modulation-salience-colorise-waveform-color-plot'>
+              <h1>modulation.salience.colorise_waveform.color_plot<a class='headerlink' href='#modulation-salience-colorise-waveform-color-plot' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.salience.colorise_waveform.color_plot'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.salience.colorise_waveform.</span></span><span class='sig-name descname'><span class='pre'>color_plot</span></span><span class='sig-paren'>(</span><em class='sig-param'><span class='n'><span class='pre'>xy:</span> <span class='pre'>~numpy.ndarray</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>colors:</span> <span class='pre'>~typing.Optional[~typing.Sequence[~typing.Union[int</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>float]]]</span> <span class='pre'>=</span> <span class='pre'>None</span></span></em>, <em class='sig-param'><span class='n'><span class='pre'>cmap=&lt;matplotlib.colors.LinearSegmentedColormap</span> <span class='pre'>object&gt;</span></span></em><span class='sig-paren'>)</span> <span class='sig-return'><span class='sig-return-icon'>&#x2192;</span> <span class='sig-return-typehint'><a class='reference external'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               href='https://docs.python.org/3/library/constants.html#None'
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               title='(in Python v3.10)'><span
+                    class='pre'>None</span></a></span></span><a class='reference internal' href='../_modules/modulation/salience/colorise_waveform.html#color_plot'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.salience.colorise_waveform.color_plot' title='Permalink to this definition'>¶</a></dt>
+                <dd>
+                  <dl class='field-list simple'>
+                    <dt class='field-odd'>Parameters</dt>
+                    <dd class='field-odd'>
+                      <ul class='simple'>
+                        <li><p><strong>xy</strong> – </p></li>
+                        <li><p><strong>colors</strong> – </p></li>
+                        <li><p><strong>cmap</strong> – </p></li>
+                      </ul>
+                    </dd>
+                  </dl>
+                </dd>
+              </dl>
+
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+                        <li class='toctree-l4'><a class='reference internal' href='modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components.html'>modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components</a></li>
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diff --git a/docs/github/html/generated/modulation.salience.colorise_waveform.colorise_waveform_by_salience.html b/docs/github/html/generated/modulation.salience.colorise_waveform.colorise_waveform_by_salience.html
new file mode 100644
index 0000000..2cca832
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+++ b/docs/github/html/generated/modulation.salience.colorise_waveform.colorise_waveform_by_salience.html
@@ -0,0 +1,144 @@
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+              <h1>modulation.salience.colorise_waveform.colorise_waveform_by_salience<a class='headerlink' href='#modulation-salience-colorise-waveform-colorise-waveform-by-salience' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.salience.colorise_waveform.colorise_waveform_by_salience'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.salience.colorise_waveform.</span></span><span class='sig-name descname'><span class='pre'>colorise_waveform_by_salience</span></span><span class='sig-paren'>(</span><span class='sig-paren'>)</span><a class='reference internal' href='../_modules/modulation/salience/colorise_waveform.html#colorise_waveform_by_salience'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.salience.colorise_waveform.colorise_waveform_by_salience' title='Permalink to this definition'>¶</a></dt>
+                <dd></dd>
+              </dl>
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+                        <li class='toctree-l4 current'><a class='current reference internal' href='#'>modulation.salience.colorise_waveform.colorise_waveform_by_salience</a></li>
+                        <li class='toctree-l4'><a class='reference internal' href='modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components.html'>modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components</a></li>
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+                            <ul>
+                              <li>Previous: <a href='modulation.salience.colorise_waveform.color_plot.html' title='previous chapter'>modulation.salience.colorise_waveform.color_plot</a></li>
+                              <li>Next: <a href='modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components.html' title='next chapter'>modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components</a></li>
+                            </ul>
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+         rel='nofollow'>Page source</a>
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diff --git a/docs/github/html/generated/modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components.html b/docs/github/html/generated/modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components.html
new file mode 100644
index 0000000..39f91d5
--- /dev/null
+++ b/docs/github/html/generated/modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components.html
@@ -0,0 +1,144 @@
+<!DOCTYPE html>
+
+<html lang='en'>
+  <head>
+    <meta charset='utf-8'/>
+    <meta name='viewport' content='width=device-width, initial-scale=1.0'/>
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+
+    <title>modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components &#8212; modulation 0.0.1 documentation</title>
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+            <section id='modulation-salience-colorise-waveform-colorise-waveform-frequency-by-components'>
+              <h1>modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components<a class='headerlink' href='#modulation-salience-colorise-waveform-colorise-waveform-frequency-by-components' title='Permalink to this heading'>¶</a></h1>
+              <dl class='py function'>
+                <dt class='sig sig-object py' id='modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components'>
+                  <span class='sig-prename descclassname'><span class='pre'>modulation.salience.colorise_waveform.</span></span><span class='sig-name descname'><span class='pre'>colorise_waveform_frequency_by_components</span></span><span class='sig-paren'>(</span><span class='sig-paren'>)</span><a class='reference internal' href='../_modules/modulation/salience/colorise_waveform.html#colorise_waveform_frequency_by_components'><span class='viewcode-link'><span class='pre'>[source]</span></span></a><a class='headerlink' href='#modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components' title='Permalink to this definition'>¶</a></dt>
+                <dd></dd>
+              </dl>
+
+            </section>
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+
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+                        <li class='toctree-l4 current'><a class='current reference internal' href='#'>modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components</a></li>
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+                            <ul>
+                              <li>Previous: <a href='modulation.salience.colorise_waveform.colorise_waveform_by_salience.html' title='previous chapter'>modulation.salience.colorise_waveform.colorise_waveform_by_salience</a></li>
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diff --git a/docs/github/html/generated/modulation.salience.colorise_waveform.html b/docs/github/html/generated/modulation.salience.colorise_waveform.html
new file mode 100644
index 0000000..0fbf912
--- /dev/null
+++ b/docs/github/html/generated/modulation.salience.colorise_waveform.html
@@ -0,0 +1,161 @@
+<!DOCTYPE html>
+
+<html lang='en'>
+  <head>
+    <meta charset='utf-8'/>
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+
+            <section id='module-modulation.salience.colorise_waveform'>
+              <span id='modulation-salience-colorise-waveform'></span>
+              <h1>modulation.salience.colorise_waveform<a class='headerlink' href='#module-modulation.salience.colorise_waveform' title='Permalink to this heading'>¶</a></h1>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.salience.colorise_waveform.color_plot.html#modulation.salience.colorise_waveform.color_plot' title='modulation.salience.colorise_waveform.color_plot'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>color_plot</span></code></a>(xy[, colors, cmap])</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param xy</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.salience.colorise_waveform.colorise_waveform_by_salience.html#modulation.salience.colorise_waveform.colorise_waveform_by_salience' title='modulation.salience.colorise_waveform.colorise_waveform_by_salience'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>colorise_waveform_by_salience</span></code></a>()</p></td>
+                    <td><p></p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components.html#modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components' title='modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>colorise_waveform_frequency_by_components</span></code></a>()</p></td>
+                    <td><p></p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
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+                        <li class='toctree-l4'><a class='reference internal' href='modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components.html'>modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components</a></li>
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diff --git a/docs/github/html/generated/modulation.salience.html b/docs/github/html/generated/modulation.salience.html
new file mode 100644
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+              <h1>modulation.salience<a class='headerlink' href='#module-modulation.salience' title='Permalink to this heading'>¶</a></h1>
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+                    <td><p></p></td>
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diff --git a/docs/github/html/generated/modulation.signal_utilities.chopping.html b/docs/github/html/generated/modulation.signal_utilities.chopping.html
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--- a/docs/github/html/generated/modulation.signal_utilities.chopping.html
+++ b/docs/github/html/generated/modulation.signal_utilities.chopping.html
@@ -1,208 +1,219 @@
-
 <!DOCTYPE html>
 
-<html lang="en">
+<html lang='en'>
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     <title>modulation.signal_utilities.chopping &#8212; modulation 0.0.1 documentation</title>
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-            
-  <section id="module-modulation.signal_utilities.chopping">
-<span id="modulation-signal-utilities-chopping"></span><h1>modulation.signal_utilities.chopping<a class="headerlink" href="#module-modulation.signal_utilities.chopping" title="Permalink to this heading">¶</a></h1>
-<p>Created on 7/8/22</p>
-<p class="rubric">Functions</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.signal_utilities.chopping.chop_n.html#modulation.signal_utilities.chopping.chop_n" title="modulation.signal_utilities.chopping.chop_n"><code class="xref py py-obj docutils literal notranslate"><span class="pre">chop_n</span></code></a>(x, n)</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param x</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.signal_utilities.chopping.chop_n_end.html#modulation.signal_utilities.chopping.chop_n_end" title="modulation.signal_utilities.chopping.chop_n_end"><code class="xref py py-obj docutils literal notranslate"><span class="pre">chop_n_end</span></code></a>(x, n)</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param x</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.signal_utilities.chopping.chop_n_middle.html#modulation.signal_utilities.chopping.chop_n_middle" title="modulation.signal_utilities.chopping.chop_n_middle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">chop_n_middle</span></code></a>(x, n)</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param x</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.signal_utilities.chopping.chop_n_middle_end.html#modulation.signal_utilities.chopping.chop_n_middle_end" title="modulation.signal_utilities.chopping.chop_n_middle_end"><code class="xref py py-obj docutils literal notranslate"><span class="pre">chop_n_middle_end</span></code></a>(x, n)</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param x</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.signal_utilities.chopping.chop_n_middle_start.html#modulation.signal_utilities.chopping.chop_n_middle_start" title="modulation.signal_utilities.chopping.chop_n_middle_start"><code class="xref py py-obj docutils literal notranslate"><span class="pre">chop_n_middle_start</span></code></a>(x, n)</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param x</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.signal_utilities.chopping.chop_n_start.html#modulation.signal_utilities.chopping.chop_n_start" title="modulation.signal_utilities.chopping.chop_n_start"><code class="xref py py-obj docutils literal notranslate"><span class="pre">chop_n_start</span></code></a>(x, n)</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param x</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.signal_utilities.chopping.mask_chop_segments.html#modulation.signal_utilities.chopping.mask_chop_segments" title="modulation.signal_utilities.chopping.mask_chop_segments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mask_chop_segments</span></code></a>(mask, data)</p></td>
-<td><p>discards rest of the data if vad is shorter than data</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.signal_utilities.chopping.separate_mask_regions.html#modulation.signal_utilities.chopping.separate_mask_regions" title="modulation.signal_utilities.chopping.separate_mask_regions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">separate_mask_regions</span></code></a>(data, mask)</p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-</section>
+    <link href='../_static/pygments.css' rel='stylesheet' type='text/css'/>
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+
+            <section id='module-modulation.signal_utilities.chopping'>
+              <span id='modulation-signal-utilities-chopping'></span>
+              <h1>modulation.signal_utilities.chopping<a class='headerlink' href='#module-modulation.signal_utilities.chopping' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 7/8/22</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.chopping.chop_n.html#modulation.signal_utilities.chopping.chop_n' title='modulation.signal_utilities.chopping.chop_n'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>chop_n</span></code></a>(x, n)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param x</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.chopping.chop_n_end.html#modulation.signal_utilities.chopping.chop_n_end' title='modulation.signal_utilities.chopping.chop_n_end'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>chop_n_end</span></code></a>(x, n)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param x</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.chopping.chop_n_middle.html#modulation.signal_utilities.chopping.chop_n_middle' title='modulation.signal_utilities.chopping.chop_n_middle'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>chop_n_middle</span></code></a>(x, n)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param x</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.chopping.chop_n_middle_end.html#modulation.signal_utilities.chopping.chop_n_middle_end' title='modulation.signal_utilities.chopping.chop_n_middle_end'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>chop_n_middle_end</span></code></a>(x, n)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param x</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.chopping.chop_n_middle_start.html#modulation.signal_utilities.chopping.chop_n_middle_start' title='modulation.signal_utilities.chopping.chop_n_middle_start'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>chop_n_middle_start</span></code></a>(x, n)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param x</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.chopping.chop_n_start.html#modulation.signal_utilities.chopping.chop_n_start' title='modulation.signal_utilities.chopping.chop_n_start'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>chop_n_start</span></code></a>(x, n)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param x</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.chopping.mask_chop_segments.html#modulation.signal_utilities.chopping.mask_chop_segments' title='modulation.signal_utilities.chopping.mask_chop_segments'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>mask_chop_segments</span></code></a>(mask, data)</p></td>
+                    <td><p>discards rest of the data if vad is shorter than data</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.chopping.separate_mask_regions.html#modulation.signal_utilities.chopping.separate_mask_regions' title='modulation.signal_utilities.chopping.separate_mask_regions'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>separate_mask_regions</span></code></a>(data, mask)</p></td>
+                    <td><p></p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
 
 
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-  <section id="module-modulation.signal_utilities">
-<span id="modulation-signal-utilities"></span><h1>modulation.signal_utilities<a class="headerlink" href="#module-modulation.signal_utilities" title="Permalink to this heading">¶</a></h1>
-<p>Created on 7/8/22</p>
-<p class="rubric">Modules</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.signal_utilities.audio_utilities.html#module-modulation.signal_utilities.audio_utilities" title="modulation.signal_utilities.audio_utilities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.signal_utilities.audio_utilities</span></code></a></p></td>
-<td><p>Created on 11-01-2021</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.signal_utilities.chopping.html#module-modulation.signal_utilities.chopping" title="modulation.signal_utilities.chopping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.signal_utilities.chopping</span></code></a></p></td>
-<td><p>Created on 7/8/22</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.signal_utilities.noise_generation.html#module-modulation.signal_utilities.noise_generation" title="modulation.signal_utilities.noise_generation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.signal_utilities.noise_generation</span></code></a></p></td>
-<td><p>Created on 09-12-2020</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.signal_utilities.segmentation.html#module-modulation.signal_utilities.segmentation" title="modulation.signal_utilities.segmentation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.signal_utilities.segmentation</span></code></a></p></td>
-<td><p>Created on 8/1/22</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.signal_utilities.splitting.html#module-modulation.signal_utilities.splitting" title="modulation.signal_utilities.splitting"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.signal_utilities.splitting</span></code></a></p></td>
-<td><p>Created on 15-01-2021</p></td>
-</tr>
-</tbody>
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-</section>
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+            <section id='module-modulation.signal_utilities'>
+              <span id='modulation-signal-utilities'></span>
+              <h1>modulation.signal_utilities<a class='headerlink' href='#module-modulation.signal_utilities' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 7/8/22</p>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.audio_utilities.html#module-modulation.signal_utilities.audio_utilities' title='modulation.signal_utilities.audio_utilities'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.signal_utilities.audio_utilities</span></code></a></p></td>
+                    <td><p>Created on 11-01-2021</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.chopping.html#module-modulation.signal_utilities.chopping' title='modulation.signal_utilities.chopping'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.signal_utilities.chopping</span></code></a></p></td>
+                    <td><p>Created on 7/8/22</p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.noise_generation.html#module-modulation.signal_utilities.noise_generation' title='modulation.signal_utilities.noise_generation'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.signal_utilities.noise_generation</span></code></a></p></td>
+                    <td><p>Created on 09-12-2020</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.segmentation.html#module-modulation.signal_utilities.segmentation' title='modulation.signal_utilities.segmentation'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.signal_utilities.segmentation</span></code></a></p></td>
+                    <td><p>Created on 8/1/22</p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.splitting.html#module-modulation.signal_utilities.splitting' title='modulation.signal_utilities.splitting'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.signal_utilities.splitting</span></code></a></p></td>
+                    <td><p>Created on 15-01-2021</p></td>
+                  </tr>
+                </tbody>
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diff --git a/docs/github/html/generated/modulation.signal_utilities.segmentation.html b/docs/github/html/generated/modulation.signal_utilities.segmentation.html
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--- a/docs/github/html/generated/modulation.signal_utilities.segmentation.html
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-  <section id="module-modulation.signal_utilities.segmentation">
-<span id="modulation-signal-utilities-segmentation"></span><h1>modulation.signal_utilities.segmentation<a class="headerlink" href="#module-modulation.signal_utilities.segmentation" title="Permalink to this heading">¶</a></h1>
-<p>Created on 8/1/22</p>
-<p class="rubric">Functions</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
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-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.signal_utilities.segmentation.runs_of_non_zero.html#modulation.signal_utilities.segmentation.runs_of_non_zero" title="modulation.signal_utilities.segmentation.runs_of_non_zero"><code class="xref py py-obj docutils literal notranslate"><span class="pre">runs_of_non_zero</span></code></a>(bits)</p></td>
-<td><p>Finds runs of non-zero bits in a sequence.</p></td>
-</tr>
-</tbody>
-</table>
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+              <span id='modulation-signal-utilities-segmentation'></span>
+              <h1>modulation.signal_utilities.segmentation<a class='headerlink' href='#module-modulation.signal_utilities.segmentation' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 8/1/22</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.segmentation.runs_of_non_zero.html#modulation.signal_utilities.segmentation.runs_of_non_zero' title='modulation.signal_utilities.segmentation.runs_of_non_zero'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>runs_of_non_zero</span></code></a>(bits)</p></td>
+                    <td><p>Finds runs of non-zero bits in a sequence.</p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
 
 
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-  <section id="module-modulation.signal_utilities.splitting">
-<span id="modulation-signal-utilities-splitting"></span><h1>modulation.signal_utilities.splitting<a class="headerlink" href="#module-modulation.signal_utilities.splitting" title="Permalink to this heading">¶</a></h1>
-<p>Created on 15-01-2021</p>
-<p class="rubric">Functions</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.signal_utilities.splitting.mask_split_non_zero_concat.html#modulation.signal_utilities.splitting.mask_split_non_zero_concat" title="modulation.signal_utilities.splitting.mask_split_non_zero_concat"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mask_split_non_zero_concat</span></code></a>(mask, data)</p></td>
-<td><p>discards rest of the data if mask is shorter than data a is where the mask is 1 b is where the mask is 0</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.signal_utilities.splitting.mask_split_non_zero_segments.html#modulation.signal_utilities.splitting.mask_split_non_zero_segments" title="modulation.signal_utilities.splitting.mask_split_non_zero_segments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mask_split_non_zero_segments</span></code></a>(mask, data)</p></td>
-<td><p>discards rest of the data if vad is shorter than data</p></td>
-</tr>
-</tbody>
-</table>
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+            <section id='module-modulation.signal_utilities.splitting'>
+              <span id='modulation-signal-utilities-splitting'></span>
+              <h1>modulation.signal_utilities.splitting<a class='headerlink' href='#module-modulation.signal_utilities.splitting' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 15-01-2021</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.splitting.mask_split_non_zero_concat.html#modulation.signal_utilities.splitting.mask_split_non_zero_concat' title='modulation.signal_utilities.splitting.mask_split_non_zero_concat'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>mask_split_non_zero_concat</span></code></a>(mask, data)</p></td>
+                    <td><p>discards rest of the data if mask is shorter than data a is where the mask is 1 b is where the mask is 0</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.signal_utilities.splitting.mask_split_non_zero_segments.html#modulation.signal_utilities.splitting.mask_split_non_zero_segments' title='modulation.signal_utilities.splitting.mask_split_non_zero_segments'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>mask_split_non_zero_segments</span></code></a>(mask, data)</p></td>
+                    <td><p>discards rest of the data if vad is shorter than data</p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
 
 
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diff --git a/docs/github/html/generated/modulation.torch_utilities.collation.html b/docs/github/html/generated/modulation.torch_utilities.collation.html
index 3f58383..2cecacf 100644
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+++ b/docs/github/html/generated/modulation.torch_utilities.collation.html
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-  <section id="module-modulation.torch_utilities.collation">
-<span id="modulation-torch-utilities-collation"></span><h1>modulation.torch_utilities.collation<a class="headerlink" href="#module-modulation.torch_utilities.collation" title="Permalink to this heading">¶</a></h1>
-<p>Created on 03-12-2020</p>
-<p class="rubric">Functions</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.torch_utilities.collation.collate_pad_wrapped.html#modulation.torch_utilities.collation.collate_pad_wrapped" title="modulation.torch_utilities.collation.collate_pad_wrapped"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collate_pad_wrapped</span></code></a>([device])</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param device</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.torch_utilities.collation.collate_transform_wrapped.html#modulation.torch_utilities.collation.collate_transform_wrapped" title="modulation.torch_utilities.collation.collate_transform_wrapped"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collate_transform_wrapped</span></code></a>(mapping_func, ...)</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param mapping_func</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-</tbody>
-</table>
-</section>
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+            <section id='module-modulation.torch_utilities.collation'>
+              <span id='modulation-torch-utilities-collation'></span>
+              <h1>modulation.torch_utilities.collation<a class='headerlink' href='#module-modulation.torch_utilities.collation' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 03-12-2020</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.torch_utilities.collation.collate_pad_wrapped.html#modulation.torch_utilities.collation.collate_pad_wrapped' title='modulation.torch_utilities.collation.collate_pad_wrapped'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>collate_pad_wrapped</span></code></a>([device])</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param device</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.torch_utilities.collation.collate_transform_wrapped.html#modulation.torch_utilities.collation.collate_transform_wrapped' title='modulation.torch_utilities.collation.collate_transform_wrapped'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>collate_transform_wrapped</span></code></a>(mapping_func, ...)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param mapping_func</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
 
 
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-<p>Created on 17-12-2020</p>
-<p class="rubric">Modules</p>
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-<td><p>Created on 03-12-2020</p></td>
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-<td><p>Created on 01-12-2020</p></td>
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+              <h1>modulation.torch_utilities<a class='headerlink' href='#module-modulation.torch_utilities' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 17-12-2020</p>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
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+                </colgroup>
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+                    <td><p><a class='reference internal' href='modulation.torch_utilities.collation.html#module-modulation.torch_utilities.collation' title='modulation.torch_utilities.collation'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.torch_utilities.collation</span></code></a></p></td>
+                    <td><p>Created on 03-12-2020</p></td>
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+                    <td><p><a class='reference internal' href='modulation.torch_utilities.padding.html#module-modulation.torch_utilities.padding' title='modulation.torch_utilities.padding'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.torch_utilities.padding</span></code></a></p></td>
+                    <td><p>Created on 01-12-2020</p></td>
+                  </tr>
+                  <tr class='row-odd'>
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+          <script>document.getElementById('searchbox').style.display = "block"</script>
 
 
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-      
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
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diff --git a/docs/github/html/generated/modulation.torch_utilities.padding.html b/docs/github/html/generated/modulation.torch_utilities.padding.html
index d58a154..2e3cff3 100644
--- a/docs/github/html/generated/modulation.torch_utilities.padding.html
+++ b/docs/github/html/generated/modulation.torch_utilities.padding.html
@@ -1,174 +1,177 @@
-
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-<html lang="en">
+<html lang='en'>
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+    <meta charset='utf-8'/>
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+    <meta content='Docutils 0.17.1: http://docutils.sourceforge.net/' name='generator'/>
 
     <title>modulation.torch_utilities.padding &#8212; modulation 0.0.1 documentation</title>
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-        <div class="bodywrapper">
-          
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-          <div class="body" role="main">
-            
-  <section id="module-modulation.torch_utilities.padding">
-<span id="modulation-torch-utilities-padding"></span><h1>modulation.torch_utilities.padding<a class="headerlink" href="#module-modulation.torch_utilities.padding" title="Permalink to this heading">¶</a></h1>
-<p>Created on 01-12-2020</p>
-<p class="rubric">Functions</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.torch_utilities.padding.batch_pad.html#modulation.torch_utilities.padding.batch_pad" title="modulation.torch_utilities.padding.batch_pad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">batch_pad</span></code></a>(image_batch, mask_batch)</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param image_batch</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.torch_utilities.padding.pad_sequence.html#modulation.torch_utilities.padding.pad_sequence" title="modulation.torch_utilities.padding.pad_sequence"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pad_sequence</span></code></a>(batch)</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param batch</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.torch_utilities.padding.min_length_pad.html#modulation.torch_utilities.padding.min_length_pad" title="modulation.torch_utilities.padding.min_length_pad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_length_pad</span></code></a>(sequence, min_length[, ...])</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param sequence</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-</tbody>
-</table>
-</section>
+    <link href='../_static/pygments.css' rel='stylesheet' type='text/css'/>
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+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
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+          <div class='body' role='main'>
+
+            <section id='module-modulation.torch_utilities.padding'>
+              <span id='modulation-torch-utilities-padding'></span>
+              <h1>modulation.torch_utilities.padding<a class='headerlink' href='#module-modulation.torch_utilities.padding' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 01-12-2020</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.torch_utilities.padding.batch_pad.html#modulation.torch_utilities.padding.batch_pad' title='modulation.torch_utilities.padding.batch_pad'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>batch_pad</span></code></a>(image_batch, mask_batch)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param image_batch</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.torch_utilities.padding.pad_sequence.html#modulation.torch_utilities.padding.pad_sequence' title='modulation.torch_utilities.padding.pad_sequence'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>pad_sequence</span></code></a>(batch)</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param batch</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.torch_utilities.padding.min_length_pad.html#modulation.torch_utilities.padding.min_length_pad' title='modulation.torch_utilities.padding.min_length_pad'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>min_length_pad</span></code></a>(sequence, min_length[, ...])</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param sequence</dt>
+                        <dd class='field-odd'><p></p></dd>
+                      </dl>
+                      </p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
 
 
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+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
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diff --git a/docs/github/html/generated/modulation.visualisation.colorise_waveform.html b/docs/github/html/generated/modulation.visualisation.colorise_waveform.html
index 42be6a6..85ec1f8 100644
--- a/docs/github/html/generated/modulation.visualisation.colorise_waveform.html
+++ b/docs/github/html/generated/modulation.visualisation.colorise_waveform.html
@@ -1,157 +1,156 @@
-
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-<html lang="en">
+<html lang='en'>
   <head>
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-            
-  <section id="module-modulation.visualisation.colorise_waveform">
-<span id="modulation-visualisation-colorise-waveform"></span><h1>modulation.visualisation.colorise_waveform<a class="headerlink" href="#module-modulation.visualisation.colorise_waveform" title="Permalink to this heading">¶</a></h1>
-<p>Created on 29/03/2020</p>
-<p class="rubric">Functions</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.visualisation.colorise_waveform.color_plot.html#modulation.visualisation.colorise_waveform.color_plot" title="modulation.visualisation.colorise_waveform.color_plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">color_plot</span></code></a>(xy[, colors, cmap])</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param xy</dt>
-<dd class="field-odd"><p></p></dd>
-</dl>
-</p></td>
-</tr>
-</tbody>
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+              <h1>modulation.visualisation.colorise_waveform<a class='headerlink' href='#module-modulation.visualisation.colorise_waveform' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 29/03/2020</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
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+                    <td><p><a class='reference internal' href='modulation.visualisation.colorise_waveform.color_plot.html#modulation.visualisation.colorise_waveform.color_plot' title='modulation.visualisation.colorise_waveform.color_plot'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>color_plot</span></code></a>(xy[, colors, cmap])</p></td>
+                    <td><p>
+                      <dl class='field-list simple'>
+                        <dt class='field-odd'>param xy</dt>
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-<span id="modulation-visualisation"></span><h1>modulation.visualisation<a class="headerlink" href="#module-modulation.visualisation" title="Permalink to this heading">¶</a></h1>
-<p>Created on 8/2/22</p>
-<p class="rubric">Modules</p>
-<table class="autosummary longtable docutils align-default">
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-<col style="width: 10%" />
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-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.visualisation.colorise_waveform.html#module-modulation.visualisation.colorise_waveform" title="modulation.visualisation.colorise_waveform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modulation.visualisation.colorise_waveform</span></code></a></p></td>
-<td><p>Created on 29/03/2020</p></td>
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+              <h1>modulation.visualisation<a class='headerlink' href='#module-modulation.visualisation' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 8/2/22</p>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.visualisation.colorise_waveform.html#module-modulation.visualisation.colorise_waveform' title='modulation.visualisation.colorise_waveform'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.visualisation.colorise_waveform</span></code></a></p></td>
+                    <td><p>Created on 29/03/2020</p></td>
+                  </tr>
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diff --git a/docs/github/html/generated/modulation.visualisation.salience.colored.html b/docs/github/html/generated/modulation.visualisation.salience.colored.html
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--- a/docs/github/html/generated/modulation.visualisation.salience.colored.html
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-            
-  <section id="module-modulation.visualisation.salience.colored">
-<span id="modulation-visualisation-salience-colored"></span><h1>modulation.visualisation.salience.colored<a class="headerlink" href="#module-modulation.visualisation.salience.colored" title="Permalink to this heading">¶</a></h1>
-<p>Created on 8/2/22</p>
-<p class="rubric">Functions</p>
-<table class="autosummary longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="modulation.visualisation.salience.colored.colorise_waveform_by_salience.html#modulation.visualisation.salience.colored.colorise_waveform_by_salience" title="modulation.visualisation.salience.colored.colorise_waveform_by_salience"><code class="xref py py-obj docutils literal notranslate"><span class="pre">colorise_waveform_by_salience</span></code></a>()</p></td>
-<td><p>description</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="modulation.visualisation.salience.colored.colorise_waveform_frequency_by_components.html#modulation.visualisation.salience.colored.colorise_waveform_frequency_by_components" title="modulation.visualisation.salience.colored.colorise_waveform_frequency_by_components"><code class="xref py py-obj docutils literal notranslate"><span class="pre">colorise_waveform_frequency_by_components</span></code></a>()</p></td>
-<td><p>description</p></td>
-</tr>
-</tbody>
-</table>
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+              <span id='modulation-visualisation-salience-colored'></span>
+              <h1>modulation.visualisation.salience.colored<a class='headerlink' href='#module-modulation.visualisation.salience.colored' title='Permalink to this heading'>¶</a></h1>
+              <p>Created on 8/2/22</p>
+              <p class='rubric'>Functions</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.visualisation.salience.colored.colorise_waveform_by_salience.html#modulation.visualisation.salience.colored.colorise_waveform_by_salience' title='modulation.visualisation.salience.colored.colorise_waveform_by_salience'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>colorise_waveform_by_salience</span></code></a>()</p></td>
+                    <td><p>description</p></td>
+                  </tr>
+                  <tr class='row-even'>
+                    <td><p><a class='reference internal' href='modulation.visualisation.salience.colored.colorise_waveform_frequency_by_components.html#modulation.visualisation.salience.colored.colorise_waveform_frequency_by_components' title='modulation.visualisation.salience.colored.colorise_waveform_frequency_by_components'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>colorise_waveform_frequency_by_components</span></code></a>()</p></td>
+                    <td><p>description</p></td>
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-<span id="modulation-visualisation-salience"></span><h1>modulation.visualisation.salience<a class="headerlink" href="#module-modulation.visualisation.salience" title="Permalink to this heading">¶</a></h1>
-<p class="rubric">Modules</p>
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+    <link href='modulation.visualisation.salience.colored.html' rel='next' title='modulation.visualisation.salience.colored'/>
+    <link href='modulation.visualisation.colorise_waveform.color_plot.html' rel='prev' title='modulation.visualisation.colorise_waveform.color_plot'/>
+
+    <link href='../_static/custom.css' rel='stylesheet' type='text/css'/>
+
+
+    <meta content='width=device-width, initial-scale=0.9, maximum-scale=0.9' name='viewport'/>
+
+  </head>
+  <body>
+
+
+    <div class='document'>
+      <div class='documentwrapper'>
+        <div class='bodywrapper'>
+
+
+          <div class='body' role='main'>
+
+            <section id='module-modulation.visualisation.salience'>
+              <span id='modulation-visualisation-salience'></span>
+              <h1>modulation.visualisation.salience<a class='headerlink' href='#module-modulation.visualisation.salience' title='Permalink to this heading'>¶</a></h1>
+              <p class='rubric'>Modules</p>
+              <table class='autosummary longtable docutils align-default'>
+                <colgroup>
+                  <col style='width: 10%'/>
+                  <col style='width: 90%'/>
+                </colgroup>
+                <tbody>
+                  <tr class='row-odd'>
+                    <td><p><a class='reference internal' href='modulation.visualisation.salience.colored.html#module-modulation.visualisation.salience.colored' title='modulation.visualisation.salience.colored'><code class='xref py py-obj docutils literal notranslate'><span class='pre'>modulation.visualisation.salience.colored</span></code></a></p></td>
+                    <td><p>Created on 8/2/22</p></td>
+                  </tr>
+                </tbody>
+              </table>
+            </section>
 
 
           </div>
-          
+
         </div>
       </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-            <p class="logo"><a href="../index.html">
-              <img class="logo" src="../_static/mod.svg" alt="Logo"/>
-            </a></p>
-<h1 class="logo"><a href="../index.html">modulation</a></h1>
-
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-<h3>Navigation</h3>
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-<li class="toctree-l2"><a class="reference internal" href="modulation.data.html">modulation.data</a></li>
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-<li class="toctree-l2 current"><a class="reference internal" href="modulation.visualisation.html">modulation.visualisation</a><ul class="current">
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-<li class="toctree-l3 current"><a class="current reference internal" href="#">modulation.visualisation.salience</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="modulation.visualisation.salience.colored.html">modulation.visualisation.salience.colored</a></li>
-</ul>
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-</ul>
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-</li>
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-<p class="caption" role="heading"><span class="caption-text">Notes</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../getting_started.html">Getting Started</a></li>
-</ul>
-
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="../index.html">Documentation overview</a><ul>
-  <li><a href="modulation.html">modulation</a><ul>
-  <li><a href="modulation.visualisation.html">modulation.visualisation</a><ul>
-      <li>Previous: <a href="modulation.visualisation.colorise_waveform.color_plot.html" title="previous chapter">modulation.visualisation.colorise_waveform.color_plot</a></li>
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-  </ul></li>
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-</ul>
-</div>
-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
-      <input type="submit" value="Go" />
-    </form>
-    </div>
-</div>
-<script>document.getElementById('searchbox').style.display = "block"</script>
-
-
-
-
-
-
+      <div aria-label='main navigation' class='sphinxsidebar' role='navigation'>
+        <div class='sphinxsidebarwrapper'>
+          <p class='logo'><a href='../index.html'>
+            <img alt='Logo' class='logo' src='../_static/mod.svg'/>
+          </a></p>
+          <h1 class='logo'><a href='../index.html'>modulation</a></h1>
+
+
+          <h3>Navigation</h3>
+          <ul class='current'>
+            <li class='toctree-l1 current'><a class='reference internal' href='modulation.html'>modulation</a>
+              <ul class='current'>
+                <li class='toctree-l2'><a class='reference internal' href='modulation.get_version.html'>modulation.get_version</a></li>
+                <li class='toctree-l2'><a class='reference internal' href='modulation.classification.html'>modulation.classification</a></li>
+                <li class='toctree-l2'><a class='reference internal' href='modulation.data.html'>modulation.data</a></li>
+                <li class='toctree-l2'><a class='reference internal' href='modulation.entry_points.html'>modulation.entry_points</a></li>
+                <li class='toctree-l2'><a class='reference internal' href='modulation.jax_utilities.html'>modulation.jax_utilities</a></li>
+                <li class='toctree-l2'><a class='reference internal' href='modulation.regression.html'>modulation.regression</a></li>
+                <li class='toctree-l2'><a class='reference internal' href='modulation.signal_utilities.html'>modulation.signal_utilities</a></li>
+                <li class='toctree-l2'><a class='reference internal' href='modulation.torch_utilities.html'>modulation.torch_utilities</a></li>
+                <li class='toctree-l2 current'><a class='reference internal' href='modulation.visualisation.html'>modulation.visualisation</a>
+                  <ul class='current'>
+                    <li class='toctree-l3'><a class='reference internal' href='modulation.visualisation.colorise_waveform.html'>modulation.visualisation.colorise_waveform</a></li>
+                    <li class='toctree-l3 current'><a class='current reference internal' href='#'>modulation.visualisation.salience</a>
+                      <ul>
+                        <li class='toctree-l4'><a class='reference internal' href='modulation.visualisation.salience.colored.html'>modulation.visualisation.salience.colored</a></li>
+                      </ul>
+                    </li>
+                  </ul>
+                </li>
+              </ul>
+            </li>
+          </ul>
+          <p class='caption' role='heading'><span class='caption-text'>Notes</span></p>
+          <ul>
+            <li class='toctree-l1'><a class='reference internal' href='../getting_started.html'>Getting Started</a></li>
+          </ul>
+
+          <div class='relations'>
+            <h3>Related Topics</h3>
+            <ul>
+              <li><a href='../index.html'>Documentation overview</a>
+                <ul>
+                  <li><a href='modulation.html'>modulation</a>
+                    <ul>
+                      <li><a href='modulation.visualisation.html'>modulation.visualisation</a>
+                        <ul>
+                          <li>Previous: <a href='modulation.visualisation.colorise_waveform.color_plot.html' title='previous chapter'>modulation.visualisation.colorise_waveform.color_plot</a></li>
+                          <li>Next: <a href='modulation.visualisation.salience.colored.html' title='next chapter'>modulation.visualisation.salience.colored</a></li>
+                        </ul>
+                      </li>
+                    </ul>
+                  </li>
+                </ul>
+              </li>
+            </ul>
+          </div>
+          <div id='searchbox' role='search' style='display: none'>
+            <h3 id='searchlabel'>Quick search</h3>
+            <div class='searchformwrapper'>
+              <form action='../search.html' class='search' method='get'>
+                <input aria-labelledby='searchlabel' autocapitalize='off' autocomplete='off' autocorrect='off' name='q' spellcheck='false' type='text'/>
+                <input type='submit' value='Go'/>
+              </form>
+            </div>
+          </div>
+          <script>document.getElementById('searchbox').style.display = "block"</script>
 
 
         </div>
       </div>
-      <div class="clearer"></div>
+      <div class='clearer'></div>
     </div>
-    <div class="footer">
+    <div class='footer'>
       &copy;.
-      
+
       |
-      Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
-      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
-      
+      Powered by <a href='http://sphinx-doc.org/'>Sphinx 5.0.2</a>
+      &amp; <a href='https://github.com/bitprophet/alabaster'>Alabaster 0.7.12</a>
+
       |
-      <a href="../_sources/generated/modulation.visualisation.salience.rst.txt"
-          rel="nofollow">Page source</a>
+      <a href='../_sources/generated/modulation.visualisation.salience.rst.txt'
+         rel='nofollow'>Page source</a>
     </div>
 
-    
 
-    
   </body>
 </html>
\ No newline at end of file
diff --git a/docs/make.sh b/docs/make.sh
old mode 100755
new mode 100644
diff --git a/docs/source/conf.py b/docs/source/conf.py
index aab8d6b..66a1b70 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -12,12 +12,12 @@
 sys.path.insert(0, str(PACKAGE_ROOT.absolute()))
 
 from modulation import (
-  PROJECT_AUTHOR,
-  PROJECT_NAME,
-  PROJECT_ORGANISATION,
-  PROJECT_YEAR,
-  PROJECT_VERSION,
-  )
+    PROJECT_AUTHOR,
+    PROJECT_NAME,
+    PROJECT_ORGANISATION,
+    PROJECT_YEAR,
+    PROJECT_VERSION,
+)
 
 # -- General configuration ------------------------------------------------
 
@@ -42,7 +42,7 @@
     "sphinx.ext.viewcode",
     "sphinx.ext.githubpages",
     "sphinx.ext.graphviz",
-    ]
+]
 
 napoleon_use_ivar = True
 autosummary_generate = True
@@ -94,7 +94,7 @@
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This patterns also effect to html_static_path and html_extra_path
-exclude_patterns = ['**/*exclude*', '**/*Exclude*']
+exclude_patterns = ["**/*exclude*", "**/*Exclude*"]
 
 # The name of the Pygments (syntax highlighting) style to use.
 highlight_language = "python"
@@ -124,7 +124,7 @@
 html_theme_options = {
     # "logo_only": True,
     # "display_version": False,
-    }
+}
 
 html_baseurl = f"{PROJECT_ORGANISATION}.github.io/{PROJECT_NAME}"
 
@@ -148,7 +148,7 @@
     # Latex figure (float) alignment
     #
     # 'figure_align': 'htbp',
-    }
+}
 
 # Grouping the document tree into LaTeX files. List of tuples
 # (source start file, target name, title,
@@ -160,8 +160,8 @@
         f"{PROJECT_NAME} Documentation",
         PROJECT_AUTHOR,
         "manual",
-        )
-    ]
+    )
+]
 
 # -- Options for manual page output ---------------------------------------
 
@@ -169,7 +169,7 @@
 # (source start file, name, description, authors, manual section).
 man_pages = [
     (master_doc, PROJECT_NAME, f"{PROJECT_NAME} Documentation", [PROJECT_AUTHOR], 1)
-    ]
+]
 
 # -- Options for Texinfo output -------------------------------------------
 
@@ -185,8 +185,8 @@
         PROJECT_NAME,
         "One line description of project.",
         "Miscellaneous",
-        )
-    ]
+    )
+]
 
 # -- Options for Epub output ----------------------------------------------
 
@@ -211,8 +211,8 @@
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {
     "python": ("https://docs.python.org/", None),
-    "numpy":  ("http://docs.scipy.org/doc/numpy/", None),
-    }
+    "numpy": ("http://docs.scipy.org/doc/numpy/", None),
+}
 
 # -- A patch that prevents Sphinx from cross-referencing ivar tags -------
 # See http://stackoverflow.com/a/41184353/3343043
@@ -223,77 +223,77 @@
 
 
 def patched_make_field(self, types, domain, items, **kw):
-  """
-
-  :param self:
-  :type self:
-  :param types:
-  :type types:
-  :param domain:
-  :type domain:
-  :param items:
-  :type items:
-  :param kw:
-  :type kw:
-  :return:
-  :rtype:
-  """
-
-  # `kw` catches `env=None` needed for newer sphinx while maintaining
-  #  backwards compatibility when passed along further down!
-  # #type: (List, unicode, Tuple) -> nodes.field
-  def handle_item(fieldarg, content):
     """
 
-    :param fieldarg:
-    :type fieldarg:
-    :param content:
-    :type content:
+    :param self:
+    :type self:
+    :param types:
+    :type types:
+    :param domain:
+    :type domain:
+    :param items:
+    :type items:
+    :param kw:
+    :type kw:
     :return:
     :rtype:
     """
-    par = nodes.paragraph()
-    par += addnodes.literal_strong("", fieldarg)  # Patch: this line added
-    # par.extend(self.make_xrefs(self.rolename, domain, fieldarg,
-    #                           addnodes.literal_strong))
-    if fieldarg in types:
-      par += nodes.Text(" (")
-      # NOTE: using .pop() here to prevent a single type node to be
-      # inserted twice into the doctree, which leads to
-      # inconsistencies later when references are resolved
-      fieldtype = types.pop(fieldarg)
-      if len(fieldtype) == 1 and isinstance(fieldtype[0], nodes.Text):
-        typename = "".join(n.astext() for n in fieldtype)
-        typename = typename.replace("int", "python:int")
-        typename = typename.replace("long", "python:long")
-        typename = typename.replace("float", "python:float")
-        typename = typename.replace("type", "python:type")
-        par.extend(
-            self.make_xrefs(
-                self.typerolename,
-                domain,
-                typename,
-                addnodes.literal_emphasis,
-                **kw,
+
+    # `kw` catches `env=None` needed for newer sphinx while maintaining
+    #  backwards compatibility when passed along further down!
+    # #type: (List, unicode, Tuple) -> nodes.field
+    def handle_item(fieldarg, content):
+        """
+
+        :param fieldarg:
+        :type fieldarg:
+        :param content:
+        :type content:
+        :return:
+        :rtype:
+        """
+        par = nodes.paragraph()
+        par += addnodes.literal_strong("", fieldarg)  # Patch: this line added
+        # par.extend(self.make_xrefs(self.rolename, domain, fieldarg,
+        #                           addnodes.literal_strong))
+        if fieldarg in types:
+            par += nodes.Text(" (")
+            # NOTE: using .pop() here to prevent a single type node to be
+            # inserted twice into the doctree, which leads to
+            # inconsistencies later when references are resolved
+            fieldtype = types.pop(fieldarg)
+            if len(fieldtype) == 1 and isinstance(fieldtype[0], nodes.Text):
+                typename = "".join(n.astext() for n in fieldtype)
+                typename = typename.replace("int", "python:int")
+                typename = typename.replace("long", "python:long")
+                typename = typename.replace("float", "python:float")
+                typename = typename.replace("type", "python:type")
+                par.extend(
+                    self.make_xrefs(
+                        self.typerolename,
+                        domain,
+                        typename,
+                        addnodes.literal_emphasis,
+                        **kw,
+                    )
                 )
-            )
-      else:
-        par += fieldtype
-      par += nodes.Text(")")
-    par += nodes.Text(" -- ")
-    par += content
-    return par
-
-  field_name = nodes.field_name("", self.label)
-  if len(items) == 1 and self.can_collapse:
-    field_arg, content = items[0]
-    body_node = handle_item(field_arg, content)
-  else:
-    body_node = self.list_type()
-    for field_arg, content in items:
-      body_node += nodes.list_item("", handle_item(field_arg, content))
-  field_body = nodes.field_body("", body_node)
-  return nodes.field("", field_name, field_body)
+            else:
+                par += fieldtype
+            par += nodes.Text(")")
+        par += nodes.Text(" -- ")
+        par += content
+        return par
+
+    field_name = nodes.field_name("", self.label)
+    if len(items) == 1 and self.can_collapse:
+        field_arg, content = items[0]
+        body_node = handle_item(field_arg, content)
+    else:
+        body_node = self.list_type()
+        for field_arg, content in items:
+            body_node += nodes.list_item("", handle_item(field_arg, content))
+    field_body = nodes.field_body("", body_node)
+    return nodes.field("", field_name, field_body)
 
 
 TypedField.make_field = patched_make_field
diff --git a/docs/source/generated/modulation.audio_utilities.funcs.de_emphasise.rst b/docs/source/generated/modulation.audio_utilities.funcs.de_emphasise.rst
new file mode 100644
index 0000000..1efde08
--- /dev/null
+++ b/docs/source/generated/modulation.audio_utilities.funcs.de_emphasise.rst
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.funcs.de\_emphasise
+===============================================
+
+.. currentmodule:: modulation.audio_utilities.funcs
+
+.. autofunction:: de_emphasise
\ No newline at end of file
diff --git a/docs/source/generated/modulation.audio_utilities.funcs.fft_frequencies.rst b/docs/source/generated/modulation.audio_utilities.funcs.fft_frequencies.rst
new file mode 100644
index 0000000..49e88ec
--- /dev/null
+++ b/docs/source/generated/modulation.audio_utilities.funcs.fft_frequencies.rst
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.funcs.fft\_frequencies
+==================================================
+
+.. currentmodule:: modulation.audio_utilities.funcs
+
+.. autofunction:: fft_frequencies
\ No newline at end of file
diff --git a/docs/source/generated/modulation.audio_utilities.funcs.hilbert_envelope.rst b/docs/source/generated/modulation.audio_utilities.funcs.hilbert_envelope.rst
new file mode 100644
index 0000000..691c557
--- /dev/null
+++ b/docs/source/generated/modulation.audio_utilities.funcs.hilbert_envelope.rst
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.funcs.hilbert\_envelope
+===================================================
+
+.. currentmodule:: modulation.audio_utilities.funcs
+
+.. autofunction:: hilbert_envelope
\ No newline at end of file
diff --git a/docs/source/generated/modulation.audio_utilities.funcs.in_mel_scale.rst b/docs/source/generated/modulation.audio_utilities.funcs.in_mel_scale.rst
new file mode 100644
index 0000000..5077e1a
--- /dev/null
+++ b/docs/source/generated/modulation.audio_utilities.funcs.in_mel_scale.rst
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.funcs.in\_mel\_scale
+================================================
+
+.. currentmodule:: modulation.audio_utilities.funcs
+
+.. autofunction:: in_mel_scale
\ No newline at end of file
diff --git a/docs/source/generated/modulation.audio_utilities.funcs.mel_scale.rst b/docs/source/generated/modulation.audio_utilities.funcs.mel_scale.rst
new file mode 100644
index 0000000..d98fade
--- /dev/null
+++ b/docs/source/generated/modulation.audio_utilities.funcs.mel_scale.rst
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.funcs.mel\_scale
+============================================
+
+.. currentmodule:: modulation.audio_utilities.funcs
+
+.. autofunction:: mel_scale
\ No newline at end of file
diff --git a/docs/source/generated/modulation.audio_utilities.funcs.pre_emphasise.rst b/docs/source/generated/modulation.audio_utilities.funcs.pre_emphasise.rst
new file mode 100644
index 0000000..66bf045
--- /dev/null
+++ b/docs/source/generated/modulation.audio_utilities.funcs.pre_emphasise.rst
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.funcs.pre\_emphasise
+================================================
+
+.. currentmodule:: modulation.audio_utilities.funcs
+
+.. autofunction:: pre_emphasise
\ No newline at end of file
diff --git a/docs/source/generated/modulation.audio_utilities.funcs.pre_emphasise_torch.rst b/docs/source/generated/modulation.audio_utilities.funcs.pre_emphasise_torch.rst
new file mode 100644
index 0000000..f555f06
--- /dev/null
+++ b/docs/source/generated/modulation.audio_utilities.funcs.pre_emphasise_torch.rst
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.funcs.pre\_emphasise\_torch
+=======================================================
+
+.. currentmodule:: modulation.audio_utilities.funcs
+
+.. autofunction:: pre_emphasise_torch
\ No newline at end of file
diff --git a/docs/source/generated/modulation.audio_utilities.funcs.rst b/docs/source/generated/modulation.audio_utilities.funcs.rst
new file mode 100644
index 0000000..a0b51f5
--- /dev/null
+++ b/docs/source/generated/modulation.audio_utilities.funcs.rst
@@ -0,0 +1,36 @@
+modulation.audio\_utilities.funcs
+=================================
+
+.. automodule:: modulation.audio_utilities.funcs
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      de_emphasise
+      fft_frequencies
+      hilbert_envelope
+      in_mel_scale
+      mel_scale
+      pre_emphasise
+      pre_emphasise_torch
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/generated/modulation.audio_utilities.rst b/docs/source/generated/modulation.audio_utilities.rst
new file mode 100644
index 0000000..8acf104
--- /dev/null
+++ b/docs/source/generated/modulation.audio_utilities.rst
@@ -0,0 +1,35 @@
+modulation.audio\_utilities
+===========================
+
+.. automodule:: modulation.audio_utilities
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom_autosummary/module.rst
+   :recursive:
+
+   modulation.audio_utilities.funcs
+   modulation.audio_utilities.persistence
+   modulation.audio_utilities.splitting
+   modulation.audio_utilities.torch_transforms
+
diff --git a/docs/source/generated/modulation.audio_utilities.splitting.mask_split_speech_silence.rst b/docs/source/generated/modulation.audio_utilities.splitting.mask_split_speech_silence.rst
new file mode 100644
index 0000000..dd7bd5d
--- /dev/null
+++ b/docs/source/generated/modulation.audio_utilities.splitting.mask_split_speech_silence.rst
@@ -0,0 +1,6 @@
+modulation.audio\_utilities.splitting.mask\_split\_speech\_silence
+==================================================================
+
+.. currentmodule:: modulation.audio_utilities.splitting
+
+.. autofunction:: mask_split_speech_silence
\ No newline at end of file
diff --git a/docs/source/generated/modulation.audio_utilities.splitting.rst b/docs/source/generated/modulation.audio_utilities.splitting.rst
new file mode 100644
index 0000000..9d2b515
--- /dev/null
+++ b/docs/source/generated/modulation.audio_utilities.splitting.rst
@@ -0,0 +1,30 @@
+modulation.audio\_utilities.splitting
+=====================================
+
+.. automodule:: modulation.audio_utilities.splitting
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      mask_split_speech_silence
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/generated/modulation.classification.metrics.lwlrap.calculate_overall_lwlrap_sklearn.rst b/docs/source/generated/modulation.classification.metrics.lwlrap.calculate_overall_lwlrap_sklearn.rst
new file mode 100644
index 0000000..53d6961
--- /dev/null
+++ b/docs/source/generated/modulation.classification.metrics.lwlrap.calculate_overall_lwlrap_sklearn.rst
@@ -0,0 +1,6 @@
+modulation.classification.metrics.lwlrap.calculate\_overall\_lwlrap\_sklearn
+============================================================================
+
+.. currentmodule:: modulation.classification.metrics.lwlrap
+
+.. autofunction:: calculate_overall_lwlrap_sklearn
\ No newline at end of file
diff --git a/docs/source/generated/modulation.classification.procs.rst b/docs/source/generated/modulation.classification.procs.rst
new file mode 100644
index 0000000..77eb33b
--- /dev/null
+++ b/docs/source/generated/modulation.classification.procs.rst
@@ -0,0 +1,31 @@
+modulation.classification.procs
+===============================
+
+.. automodule:: modulation.classification.procs
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      single_epoch_evaluation
+      single_epoch_fitting
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/generated/modulation.classification.procs.single_epoch_evaluation.rst b/docs/source/generated/modulation.classification.procs.single_epoch_evaluation.rst
new file mode 100644
index 0000000..bc9497a
--- /dev/null
+++ b/docs/source/generated/modulation.classification.procs.single_epoch_evaluation.rst
@@ -0,0 +1,6 @@
+modulation.classification.procs.single\_epoch\_evaluation
+=========================================================
+
+.. currentmodule:: modulation.classification.procs
+
+.. autofunction:: single_epoch_evaluation
\ No newline at end of file
diff --git a/docs/source/generated/modulation.classification.procs.single_epoch_fitting.rst b/docs/source/generated/modulation.classification.procs.single_epoch_fitting.rst
new file mode 100644
index 0000000..924ad56
--- /dev/null
+++ b/docs/source/generated/modulation.classification.procs.single_epoch_fitting.rst
@@ -0,0 +1,6 @@
+modulation.classification.procs.single\_epoch\_fitting
+======================================================
+
+.. currentmodule:: modulation.classification.procs
+
+.. autofunction:: single_epoch_fitting
\ No newline at end of file
diff --git a/docs/source/generated/modulation.denoising.band_pass.rst b/docs/source/generated/modulation.denoising.band_pass.rst
new file mode 100644
index 0000000..f864a05
--- /dev/null
+++ b/docs/source/generated/modulation.denoising.band_pass.rst
@@ -0,0 +1,23 @@
+modulation.denoising.band\_pass
+===============================
+
+.. automodule:: modulation.denoising.band_pass
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/generated/modulation.denoising.finger_print.rst b/docs/source/generated/modulation.denoising.finger_print.rst
new file mode 100644
index 0000000..28ec652
--- /dev/null
+++ b/docs/source/generated/modulation.denoising.finger_print.rst
@@ -0,0 +1,23 @@
+modulation.denoising.finger\_print
+==================================
+
+.. automodule:: modulation.denoising.finger_print
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/generated/modulation.denoising.rst b/docs/source/generated/modulation.denoising.rst
new file mode 100644
index 0000000..31b62ca
--- /dev/null
+++ b/docs/source/generated/modulation.denoising.rst
@@ -0,0 +1,33 @@
+modulation.denoising
+====================
+
+.. automodule:: modulation.denoising
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom_autosummary/module.rst
+   :recursive:
+
+   modulation.denoising.band_pass
+   modulation.denoising.finger_print
+
diff --git a/docs/source/generated/modulation.noise_generation.additive_noise.compute_additive_noise_samples.rst b/docs/source/generated/modulation.noise_generation.additive_noise.compute_additive_noise_samples.rst
new file mode 100644
index 0000000..b46d301
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.additive_noise.compute_additive_noise_samples.rst
@@ -0,0 +1,6 @@
+modulation.noise\_generation.additive\_noise.compute\_additive\_noise\_samples
+==============================================================================
+
+.. currentmodule:: modulation.noise_generation.additive_noise
+
+.. autofunction:: compute_additive_noise_samples
\ No newline at end of file
diff --git a/docs/source/generated/modulation.noise_generation.additive_noise.rst b/docs/source/generated/modulation.noise_generation.additive_noise.rst
new file mode 100644
index 0000000..79fdefe
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.additive_noise.rst
@@ -0,0 +1,31 @@
+modulation.noise\_generation.additive\_noise
+============================================
+
+.. automodule:: modulation.noise_generation.additive_noise
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      compute_additive_noise_samples
+      sample_noise
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/generated/modulation.noise_generation.additive_noise.sample_noise.rst b/docs/source/generated/modulation.noise_generation.additive_noise.sample_noise.rst
new file mode 100644
index 0000000..747a3e5
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.additive_noise.sample_noise.rst
@@ -0,0 +1,6 @@
+modulation.noise\_generation.additive\_noise.sample\_noise
+==========================================================
+
+.. currentmodule:: modulation.noise_generation.additive_noise
+
+.. autofunction:: sample_noise
\ No newline at end of file
diff --git a/docs/source/generated/modulation.noise_generation.babble_noise.generate_babble_noise.rst b/docs/source/generated/modulation.noise_generation.babble_noise.generate_babble_noise.rst
new file mode 100644
index 0000000..8593850
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.babble_noise.generate_babble_noise.rst
@@ -0,0 +1,6 @@
+modulation.noise\_generation.babble\_noise.generate\_babble\_noise
+==================================================================
+
+.. currentmodule:: modulation.noise_generation.babble_noise
+
+.. autofunction:: generate_babble_noise
\ No newline at end of file
diff --git a/docs/source/generated/modulation.noise_generation.babble_noise.rst b/docs/source/generated/modulation.noise_generation.babble_noise.rst
new file mode 100644
index 0000000..f3e5d69
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.babble_noise.rst
@@ -0,0 +1,30 @@
+modulation.noise\_generation.babble\_noise
+==========================================
+
+.. automodule:: modulation.noise_generation.babble_noise
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      generate_babble_noise
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/generated/modulation.noise_generation.deprecated.rst b/docs/source/generated/modulation.noise_generation.deprecated.rst
new file mode 100644
index 0000000..9e88733
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.deprecated.rst
@@ -0,0 +1,32 @@
+modulation.noise\_generation.deprecated
+=======================================
+
+.. automodule:: modulation.noise_generation.deprecated
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom_autosummary/module.rst
+   :recursive:
+
+   modulation.noise_generation.deprecated.additive_noise
+
diff --git a/docs/source/generated/modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.rst b/docs/source/generated/modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.rst
new file mode 100644
index 0000000..a9abc53
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.gaussian_noise.GaussianNoiseTypeEnum.rst
@@ -0,0 +1,29 @@
+modulation.noise\_generation.gaussian\_noise.GaussianNoiseTypeEnum
+==================================================================
+
+.. currentmodule:: modulation.noise_generation.gaussian_noise
+
+.. autoclass:: GaussianNoiseTypeEnum
+   :members:
+   :show-inheritance:
+   :inherited-members:
+
+   
+   .. automethod:: __init__
+
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~GaussianNoiseTypeEnum.white
+      ~GaussianNoiseTypeEnum.brown
+      ~GaussianNoiseTypeEnum.blue
+      ~GaussianNoiseTypeEnum.pink
+      ~GaussianNoiseTypeEnum.violet
+   
+   
\ No newline at end of file
diff --git a/docs/source/generated/modulation.noise_generation.gaussian_noise.blue_noise.rst b/docs/source/generated/modulation.noise_generation.gaussian_noise.blue_noise.rst
new file mode 100644
index 0000000..4588bf6
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.gaussian_noise.blue_noise.rst
@@ -0,0 +1,6 @@
+modulation.noise\_generation.gaussian\_noise.blue\_noise
+========================================================
+
+.. currentmodule:: modulation.noise_generation.gaussian_noise
+
+.. autofunction:: blue_noise
\ No newline at end of file
diff --git a/docs/source/generated/modulation.noise_generation.gaussian_noise.brown_noise.rst b/docs/source/generated/modulation.noise_generation.gaussian_noise.brown_noise.rst
new file mode 100644
index 0000000..d00a955
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.gaussian_noise.brown_noise.rst
@@ -0,0 +1,6 @@
+modulation.noise\_generation.gaussian\_noise.brown\_noise
+=========================================================
+
+.. currentmodule:: modulation.noise_generation.gaussian_noise
+
+.. autofunction:: brown_noise
\ No newline at end of file
diff --git a/docs/source/generated/modulation.noise_generation.gaussian_noise.generate_noise.rst b/docs/source/generated/modulation.noise_generation.gaussian_noise.generate_noise.rst
new file mode 100644
index 0000000..ac42c63
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.gaussian_noise.generate_noise.rst
@@ -0,0 +1,6 @@
+modulation.noise\_generation.gaussian\_noise.generate\_noise
+============================================================
+
+.. currentmodule:: modulation.noise_generation.gaussian_noise
+
+.. autofunction:: generate_noise
\ No newline at end of file
diff --git a/docs/source/generated/modulation.noise_generation.gaussian_noise.pink_noise.rst b/docs/source/generated/modulation.noise_generation.gaussian_noise.pink_noise.rst
new file mode 100644
index 0000000..c4ef50a
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.gaussian_noise.pink_noise.rst
@@ -0,0 +1,6 @@
+modulation.noise\_generation.gaussian\_noise.pink\_noise
+========================================================
+
+.. currentmodule:: modulation.noise_generation.gaussian_noise
+
+.. autofunction:: pink_noise
\ No newline at end of file
diff --git a/docs/source/generated/modulation.noise_generation.gaussian_noise.rst b/docs/source/generated/modulation.noise_generation.gaussian_noise.rst
new file mode 100644
index 0000000..7727e06
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.gaussian_noise.rst
@@ -0,0 +1,43 @@
+modulation.noise\_generation.gaussian\_noise
+============================================
+
+.. automodule:: modulation.noise_generation.gaussian_noise
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      blue_noise
+      brown_noise
+      generate_noise
+      pink_noise
+      violet_noise
+      white_noise
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom_autosummary/class.rst
+   
+      GaussianNoiseTypeEnum
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/generated/modulation.noise_generation.gaussian_noise.violet_noise.rst b/docs/source/generated/modulation.noise_generation.gaussian_noise.violet_noise.rst
new file mode 100644
index 0000000..521da38
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.gaussian_noise.violet_noise.rst
@@ -0,0 +1,6 @@
+modulation.noise\_generation.gaussian\_noise.violet\_noise
+==========================================================
+
+.. currentmodule:: modulation.noise_generation.gaussian_noise
+
+.. autofunction:: violet_noise
\ No newline at end of file
diff --git a/docs/source/generated/modulation.noise_generation.gaussian_noise.white_noise.rst b/docs/source/generated/modulation.noise_generation.gaussian_noise.white_noise.rst
new file mode 100644
index 0000000..ebe8cd7
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.gaussian_noise.white_noise.rst
@@ -0,0 +1,6 @@
+modulation.noise\_generation.gaussian\_noise.white\_noise
+=========================================================
+
+.. currentmodule:: modulation.noise_generation.gaussian_noise
+
+.. autofunction:: white_noise
\ No newline at end of file
diff --git a/docs/source/generated/modulation.noise_generation.rst b/docs/source/generated/modulation.noise_generation.rst
new file mode 100644
index 0000000..4662bc4
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.rst
@@ -0,0 +1,36 @@
+modulation.noise\_generation
+============================
+
+.. automodule:: modulation.noise_generation
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom_autosummary/module.rst
+   :recursive:
+
+   modulation.noise_generation.additive_noise
+   modulation.noise_generation.babble_noise
+   modulation.noise_generation.deprecated
+   modulation.noise_generation.gaussian_noise
+   modulation.noise_generation.shaped_noise
+
diff --git a/docs/source/generated/modulation.noise_generation.shaped_noise.generate_speech_shaped_noise.rst b/docs/source/generated/modulation.noise_generation.shaped_noise.generate_speech_shaped_noise.rst
new file mode 100644
index 0000000..2c5f635
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.shaped_noise.generate_speech_shaped_noise.rst
@@ -0,0 +1,6 @@
+modulation.noise\_generation.shaped\_noise.generate\_speech\_shaped\_noise
+==========================================================================
+
+.. currentmodule:: modulation.noise_generation.shaped_noise
+
+.. autofunction:: generate_speech_shaped_noise
\ No newline at end of file
diff --git a/docs/source/generated/modulation.noise_generation.shaped_noise.rst b/docs/source/generated/modulation.noise_generation.shaped_noise.rst
new file mode 100644
index 0000000..8ba3b87
--- /dev/null
+++ b/docs/source/generated/modulation.noise_generation.shaped_noise.rst
@@ -0,0 +1,30 @@
+modulation.noise\_generation.shaped\_noise
+==========================================
+
+.. automodule:: modulation.noise_generation.shaped_noise
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      generate_speech_shaped_noise
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/generated/modulation.salience.colorise_waveform.color_plot.rst b/docs/source/generated/modulation.salience.colorise_waveform.color_plot.rst
new file mode 100644
index 0000000..c1238ee
--- /dev/null
+++ b/docs/source/generated/modulation.salience.colorise_waveform.color_plot.rst
@@ -0,0 +1,6 @@
+modulation.salience.colorise\_waveform.color\_plot
+==================================================
+
+.. currentmodule:: modulation.salience.colorise_waveform
+
+.. autofunction:: color_plot
\ No newline at end of file
diff --git a/docs/source/generated/modulation.salience.colorise_waveform.colorise_waveform_by_salience.rst b/docs/source/generated/modulation.salience.colorise_waveform.colorise_waveform_by_salience.rst
new file mode 100644
index 0000000..e42e198
--- /dev/null
+++ b/docs/source/generated/modulation.salience.colorise_waveform.colorise_waveform_by_salience.rst
@@ -0,0 +1,6 @@
+modulation.salience.colorise\_waveform.colorise\_waveform\_by\_salience
+=======================================================================
+
+.. currentmodule:: modulation.salience.colorise_waveform
+
+.. autofunction:: colorise_waveform_by_salience
\ No newline at end of file
diff --git a/docs/source/generated/modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components.rst b/docs/source/generated/modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components.rst
new file mode 100644
index 0000000..1518eb4
--- /dev/null
+++ b/docs/source/generated/modulation.salience.colorise_waveform.colorise_waveform_frequency_by_components.rst
@@ -0,0 +1,6 @@
+modulation.salience.colorise\_waveform.colorise\_waveform\_frequency\_by\_components
+====================================================================================
+
+.. currentmodule:: modulation.salience.colorise_waveform
+
+.. autofunction:: colorise_waveform_frequency_by_components
\ No newline at end of file
diff --git a/docs/source/generated/modulation.salience.colorise_waveform.rst b/docs/source/generated/modulation.salience.colorise_waveform.rst
new file mode 100644
index 0000000..cbcb661
--- /dev/null
+++ b/docs/source/generated/modulation.salience.colorise_waveform.rst
@@ -0,0 +1,32 @@
+modulation.salience.colorise\_waveform
+======================================
+
+.. automodule:: modulation.salience.colorise_waveform
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      color_plot
+      colorise_waveform_by_salience
+      colorise_waveform_frequency_by_components
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/docs/source/generated/modulation.salience.rst b/docs/source/generated/modulation.salience.rst
new file mode 100644
index 0000000..5a1ae59
--- /dev/null
+++ b/docs/source/generated/modulation.salience.rst
@@ -0,0 +1,32 @@
+modulation.salience
+===================
+
+.. automodule:: modulation.salience
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: custom_autosummary/module.rst
+   :recursive:
+
+   modulation.salience.colorise_waveform
+
diff --git a/modulation/__init__.py b/modulation/__init__.py
index a93472a..59bda25 100644
--- a/modulation/__init__.py
+++ b/modulation/__init__.py
@@ -10,7 +10,7 @@
 
 __project__ = "Modulation"
 __author__ = "Christian Heider Nielsen"
-__version__ = "0.0.1"
+__version__ = "0.0.2"
 __doc__ = r"""
 .. module:: modulation
    :platform: Unix, Windows
@@ -22,23 +22,32 @@
 
 @author: cnheider
 """
-__all__ = ["PROJECT_APP_PATH", "PROJECT_NAME", "PROJECT_VERSION", "get_version", 'PROJECT_ORGANISATION',
-           'PROJECT_AUTHOR', 'PROJECT_YEAR', 'INCLUDE_PROJECT_READMES', 'PACKAGE_DATA_PATH']
+__all__ = [
+    "PROJECT_APP_PATH",
+    "PROJECT_NAME",
+    "PROJECT_VERSION",
+    "get_version",
+    "PROJECT_ORGANISATION",
+    "PROJECT_AUTHOR",
+    "PROJECT_YEAR",
+    "INCLUDE_PROJECT_READMES",
+    "PACKAGE_DATA_PATH",
+]
 
 from typing import Any
 from pathlib import Path
 
 
 def dist_is_editable(dist: Any) -> bool:
-  """
-  Return True if given Distribution is an editable installation."""
-  import sys
+    """
+    Return True if given Distribution is an editable installation."""
+    import sys
 
-  for path_item in sys.path:
-    egg_link = Path(path_item) / f"{dist.project_name}.egg-link"
-    if egg_link.is_file():
-      return True
-  return False
+    for path_item in sys.path:
+        egg_link = Path(path_item) / f"{dist.project_name}.egg-link"
+        if egg_link.is_file():
+            return True
+    return False
 
 
 PROJECT_NAME = __project__.lower().strip().replace(" ", "_")
@@ -46,70 +55,70 @@ def dist_is_editable(dist: Any) -> bool:
 PROJECT_YEAR = 2018
 PROJECT_AUTHOR = __author__.lower().strip().replace(" ", "_")
 PROJECT_ORGANISATION = "Aivclab"
-PROJECT_APP_PATH = AppPath(app_name = PROJECT_NAME, app_author = PROJECT_AUTHOR)
+PROJECT_APP_PATH = AppPath(app_name=PROJECT_NAME, app_author=PROJECT_AUTHOR)
 PACKAGE_DATA_PATH = Path(pkg_resources.resource_filename(PROJECT_NAME, "data"))
 INCLUDE_PROJECT_READMES = False
 
 distributions = {v.key: v for v in pkg_resources.working_set}
 if PROJECT_NAME in distributions:
-  distribution = distributions[PROJECT_NAME]
-  DEVELOP = dist_is_editable(distribution)
+    distribution = distributions[PROJECT_NAME]
+    DEVELOP = dist_is_editable(distribution)
 else:
-  DEVELOP = True
+    DEVELOP = True
 
 
 def get_version(append_time: Any = DEVELOP) -> str:
-  """
-
-  :param append_time:
-  :type append_time:
-  :return:
-  :rtype:
-  """
-  version = __version__
-  if not version:
-    version = os.getenv("VERSION", "0.0.0")
-
-  if append_time:
-    now = datetime.datetime.utcnow()
-    date_version = now.strftime("%Y%m%d%H%M%S")
-    # date_version = time.time()
-
-    if version:
-      # Most git tags are prefixed with 'v' (example: v1.2.3) this is
-      # never desirable for artifact repositories, so we strip the
-      # leading 'v' if it's present.
-      version = (
-          version[1:]
-          if isinstance(version, str) and version.startswith("v")
-          else version
-      )
-    else:
-      # Default version is an ISO8601 compliant datetime. PyPI doesn't allow
-      # the colon ':' character in its versions, and time is required to allow
-      # for multiple publications to master in one day. This datetime string
-      # uses the 'basic' ISO8601 format for both its date and time components
-      # to avoid issues with the colon character (ISO requires that date and
-      # time components of a date-time string must be uniformly basic or
-      # extended, which is why the date component does not have dashes.
-      #
-      # Publications using datetime versions should only be made from master
-      # to represent the HEAD moving forward.
-      warn(
-          f"Environment variable VERSION is not set, only using datetime: {date_version}"
-          )
-
-      # warn(f'Environment variable VERSION is not set, only using timestamp: {version}')
-
-    version = f"{version}.{date_version}"
-
-  return version
+    """
+
+    :param append_time:
+    :type append_time:
+    :return:
+    :rtype:
+    """
+    version = __version__
+    if not version:
+        version = os.getenv("VERSION", "0.0.0")
+
+    if append_time:
+        now = datetime.datetime.utcnow()
+        date_version = now.strftime("%Y%m%d%H%M%S")
+        # date_version = time.time()
+
+        if version:
+            # Most git tags are prefixed with 'v' (example: v1.2.3) this is
+            # never desirable for artifact repositories, so we strip the
+            # leading 'v' if it's present.
+            version = (
+                version[1:]
+                if isinstance(version, str) and version.startswith("v")
+                else version
+            )
+        else:
+            # Default version is an ISO8601 compliant datetime. PyPI doesn't allow
+            # the colon ':' character in its versions, and time is required to allow
+            # for multiple publications to master in one day. This datetime string
+            # uses the 'basic' ISO8601 format for both its date and time components
+            # to avoid issues with the colon character (ISO requires that date and
+            # time components of a date-time string must be uniformly basic or
+            # extended, which is why the date component does not have dashes.
+            #
+            # Publications using datetime versions should only be made from master
+            # to represent the HEAD moving forward.
+            warn(
+                f"Environment variable VERSION is not set, only using datetime: {date_version}"
+            )
+
+            # warn(f'Environment variable VERSION is not set, only using timestamp: {version}')
+
+        version = f"{version}.{date_version}"
+
+    return version
 
 
 if __version__ is None:
-  __version__ = get_version(append_time = True)
+    __version__ = get_version(append_time=True)
 
 __version_info__ = tuple(int(segment) for segment in __version__.split("."))
 
 if __name__ == "__main__":
-  print(PROJECT_APP_PATH.user_log)
+    print(PROJECT_APP_PATH.user_log)
diff --git a/modulation/classification/architecture/__init__.py b/modulation/classification/architecture/__init__.py
index 80f1592..aa49b7b 100644
--- a/modulation/classification/architecture/__init__.py
+++ b/modulation/classification/architecture/__init__.py
@@ -7,5 +7,5 @@
            Created on 18-11-2020
            """
 
+from .cepstral_a import *
 from .m5 import *
-from .cepstral_a import *
\ No newline at end of file
diff --git a/modulation/classification/metrics/__init__.py b/modulation/classification/metrics/__init__.py
index 564bcbb..d54c493 100644
--- a/modulation/classification/metrics/__init__.py
+++ b/modulation/classification/metrics/__init__.py
@@ -8,4 +8,4 @@
            """
 
 
-from .lwlrap import *
\ No newline at end of file
+from .lwlrap import *
diff --git a/modulation/classification/metrics/lwlrap.py b/modulation/classification/metrics/lwlrap.py
index c21201a..e9fd003 100644
--- a/modulation/classification/metrics/lwlrap.py
+++ b/modulation/classification/metrics/lwlrap.py
@@ -11,7 +11,12 @@
 import sklearn
 
 
-__all__ = ['one_sample_positive_class_precisions','lwlrap_accumulator','calculate_per_class_lwlrap']
+__all__ = [
+    "one_sample_positive_class_precisions",
+    "lwlrap_accumulator",
+    "calculate_per_class_lwlrap",
+]
+
 
 def one_sample_positive_class_precisions(
     scores: numpy.array, truths: numpy.array
@@ -129,8 +134,6 @@ def overall_lwlrap(self):
         return numpy.sum(self.per_class_lwlrap() * self.per_class_weight())
 
 
-
-
 def calculate_per_class_lwlrap(truth, scores):
     """Calculate label-weighted label-ranking average precision.
 
diff --git a/modulation/classification/procedure.py b/modulation/classification/procedure.py
index 7366787..b11e281 100644
--- a/modulation/classification/procedure.py
+++ b/modulation/classification/procedure.py
@@ -10,17 +10,17 @@
 import torch
 from draugr.numpy_utilities import SplitEnum
 from draugr.torch_utilities import (
-  TorchEvalSession,
-  TorchTrainSession,
-  global_torch_device,
-  )
+    TorchEvalSession,
+    TorchTrainSession,
+    global_torch_device,
+)
 from draugr.visualisation import progress_bar
 from draugr.writers import Writer
 from torch.nn import Module
 from torch.nn.functional import nll_loss
 from torch.utils.data import DataLoader
 
-__all__ = ['single_epoch_fitting', 'single_epoch_evaluation']
+__all__ = ["single_epoch_fitting", "single_epoch_evaluation"]
 
 
 def single_epoch_fitting(
@@ -31,39 +31,39 @@ def single_epoch_fitting(
     epoch: int = None,
     writer: Writer = None,
     device_: torch.device = global_torch_device(),
-    ) -> None:
-  """
+) -> None:
+    """
 
-  :param model:
-  :type model:
-  :param optimiser:
-  :type optimiser:
-  :param train_loader_:
-  :type train_loader_:
-  :param epoch:
-  :type epoch:
-  :param writer:
-  :type writer:
-  :param device_:
-  :type device_:
-  """
-  accum_loss = 0
-  num_batches = len(train_loader_)
+    :param model:
+    :type model:
+    :param optimiser:
+    :type optimiser:
+    :param train_loader_:
+    :type train_loader_:
+    :param epoch:
+    :type epoch:
+    :param writer:
+    :type writer:
+    :param device_:
+    :type device_:
+    """
+    accum_loss = 0
+    num_batches = len(train_loader_)
 
-  with TorchTrainSession(model):
-    for batch_idx, (data, target) in progress_bar(
-        enumerate(train_loader_), description = "train batch #", total = num_batches
+    with TorchTrainSession(model):
+        for batch_idx, (data, target) in progress_bar(
+            enumerate(train_loader_), description="train batch #", total=num_batches
         ):
-      loss = nll_loss(
-          model(data.to(device_)).squeeze(), target.to(device_)
-          )  # negative log-likelihood for a tensor of size (batch x 1 x n_output)
-      optimiser.zero_grad()
-      loss.backward()
-      optimiser.step()
-      accum_loss += loss.item()
+            loss = nll_loss(
+                model(data.to(device_)).squeeze(), target.to(device_)
+            )  # negative log-likelihood for a tensor of size (batch x 1 x n_output)
+            optimiser.zero_grad()
+            loss.backward()
+            optimiser.step()
+            accum_loss += loss.item()
 
-  if writer:
-    writer.scalar("loss", accum_loss / num_batches, epoch)
+    if writer:
+        writer.scalar("loss", accum_loss / num_batches, epoch)
 
 
 def single_epoch_evaluation(
@@ -74,40 +74,40 @@ def single_epoch_evaluation(
     epoch: int = None,
     writer: Writer = None,
     device: torch.device = global_torch_device(),
-    ) -> float:
-  """
+) -> float:
+    """
 
-  :param model:
-  :type model:
-  :param evaluation_loader:
-  :type evaluation_loader:
-  :param subset:
-  :type subset:
-  :param epoch:
-  :type epoch:
-  :param writer:
-  :type writer:
-  :param device:
-  :type device:
-  :return:
-  :rtype:
-  """
-  correct = 0
-  num_batches = len(evaluation_loader)
-  with TorchEvalSession(model):
-    for data, target in progress_bar(
-        evaluation_loader, description = f"{subset} batch #", total = num_batches
+    :param model:
+    :type model:
+    :param evaluation_loader:
+    :type evaluation_loader:
+    :param subset:
+    :type subset:
+    :param epoch:
+    :type epoch:
+    :param writer:
+    :type writer:
+    :param device:
+    :type device:
+    :return:
+    :rtype:
+    """
+    correct = 0
+    num_batches = len(evaluation_loader)
+    with TorchEvalSession(model):
+        for data, target in progress_bar(
+            evaluation_loader, description=f"{subset} batch #", total=num_batches
         ):
-      correct += (
-          model(data.to(device))
-          .argmax(dim = -1)
-          .squeeze()
-          .eq(target.to(device))
-          .sum()
-          .item()
-      )
+            correct += (
+                model(data.to(device))
+                .argmax(dim=-1)
+                .squeeze()
+                .eq(target.to(device))
+                .sum()
+                .item()
+            )
 
-  acc = correct / len(evaluation_loader.dataset)
-  if writer:
-    writer.scalar(f"{subset}_accuracy", acc, epoch)
-  return acc
+    acc = correct / len(evaluation_loader.dataset)
+    if writer:
+        writer.scalar(f"{subset}_accuracy", acc, epoch)
+    return acc
diff --git a/modulation/classification/seperation/__init__.py b/modulation/classification/seperation/__init__.py
index 71a5457..92b2258 100644
--- a/modulation/classification/seperation/__init__.py
+++ b/modulation/classification/seperation/__init__.py
@@ -7,4 +7,4 @@
            Created on 18-12-2020
            """
 
-from .audio_unet import *
\ No newline at end of file
+from .audio_unet import *
diff --git a/modulation/classification/seperation/audio_unet.py b/modulation/classification/seperation/audio_unet.py
index 175fc37..a50467a 100644
--- a/modulation/classification/seperation/audio_unet.py
+++ b/modulation/classification/seperation/audio_unet.py
@@ -15,7 +15,8 @@
 
 from modulation.torch_utilities import torch_transforms
 
-__all__ = ['UNetConvBlock','UNetDeconvBlock','UNet']
+__all__ = ["UNetConvBlock", "UNetDeconvBlock", "UNet"]
+
 
 class UNetConvBlock(nn.Module):
     """description"""
diff --git a/modulation/data/__init__.py b/modulation/data/__init__.py
index 52ef0ed..f2030e7 100644
--- a/modulation/data/__init__.py
+++ b/modulation/data/__init__.py
@@ -8,4 +8,4 @@
            """
 
 from .audio import *
-from .signal_generator import *
\ No newline at end of file
+from .signal_generator import *
diff --git a/modulation/data/audio/__init__.py b/modulation/data/audio/__init__.py
index 86f4616..102870c 100644
--- a/modulation/data/audio/__init__.py
+++ b/modulation/data/audio/__init__.py
@@ -7,4 +7,4 @@
            Created on 04-05-2021
            """
 
-from .speech import *
\ No newline at end of file
+from .speech import *
diff --git a/modulation/data/audio/speech/__init__.py b/modulation/data/audio/speech/__init__.py
index 82f1cd8..cd2e8c1 100644
--- a/modulation/data/audio/speech/__init__.py
+++ b/modulation/data/audio/speech/__init__.py
@@ -7,5 +7,5 @@
            Created on 04-05-2021
            """
 
+from .recognition import *
 from .seperation import *
-from .recognition import *
\ No newline at end of file
diff --git a/modulation/data/audio/speech/recognition/libri_speech.py b/modulation/data/audio/speech/recognition/libri_speech.py
index f0c23c5..994d950 100644
--- a/modulation/data/audio/speech/recognition/libri_speech.py
+++ b/modulation/data/audio/speech/recognition/libri_speech.py
@@ -20,9 +20,8 @@ class LibriSpeech(LIBRISPEECH):
     """description"""
 
     class LibriSpeechSubsets(Enum):
-        '''
+        """ """
 
-        '''
         dev_clean = "dev-clean"
         dev_other = "dev-other"
         test_clean = "test-clean"
@@ -32,9 +31,8 @@ class LibriSpeechSubsets(Enum):
         train_other_500 = "train-other-500"
 
     class CustomSubsets(Enum):
-        '''
+        """ """
 
-        '''
         male = "M"
         female = "F"
         # TODO: add more like Splits ...
diff --git a/modulation/data/audio/speech/seperation/__init__.py b/modulation/data/audio/speech/seperation/__init__.py
index 2f130a3..ce8a931 100644
--- a/modulation/data/audio/speech/seperation/__init__.py
+++ b/modulation/data/audio/speech/seperation/__init__.py
@@ -7,4 +7,4 @@
            Created on 04-05-2021
            """
 
-from .dsd100 import *
\ No newline at end of file
+from .dsd100 import *
diff --git a/modulation/data/audio/speech/seperation/dsd100.py b/modulation/data/audio/speech/seperation/dsd100.py
index b150f4c..bd2d477 100644
--- a/modulation/data/audio/speech/seperation/dsd100.py
+++ b/modulation/data/audio/speech/seperation/dsd100.py
@@ -4,9 +4,10 @@
 
 import torch
 import torchaudio
+from torch.utils.data import Dataset
 
 
-class DSD100(torch.utils.data.Dataset):
+class DSD100(Dataset):
     """
     DSD100 dataset for audio source separation (2-stem)
     :param root: Path to the directory where the dataset is found.
diff --git a/modulation/data/signal_generator.py b/modulation/data/signal_generator.py
index 859519c..3197571 100644
--- a/modulation/data/signal_generator.py
+++ b/modulation/data/signal_generator.py
@@ -17,96 +17,95 @@
 
 
 def multifreq(x: numpy.ndarray) -> numpy.ndarray:
-  return (
-      2
-      + numpy.sin(x * numpy.pi)
-      + 0.5 * numpy.sin(2 * x * numpy.pi)
-      - 0.2 * numpy.cos(5 * x * numpy.pi)
-  )
+    return (
+        2
+        + numpy.sin(x * numpy.pi)
+        + 0.5 * numpy.sin(2 * x * numpy.pi)
+        - 0.2 * numpy.cos(5 * x * numpy.pi)
+    )
 
 
 def triangle(x: numpy.ndarray, section_length: float = 0.5) -> numpy.ndarray:
 
-  section0 = x < section_length
-  section1 = (x >= section_length) & (x < 2 * section_length)
-  section2 = (x >= 2 * section_length) & (x < 3 * section_length)
-  section3 = x >= 3 * section_length
-  output = numpy.zeros_like(x)
-  output[section0] = x[section0]
-  output[section1] = 2 * section_length - x[section1]
-  output[section2] = x[section2] - 2 * section_length
-  output[section3] = 4 * section_length - x[section3]
-  return output
+    section0 = x < section_length
+    section1 = (x >= section_length) & (x < 2 * section_length)
+    section2 = (x >= 2 * section_length) & (x < 3 * section_length)
+    section3 = x >= 3 * section_length
+    output = numpy.zeros_like(x)
+    output[section0] = x[section0]
+    output[section1] = 2 * section_length - x[section1]
+    output[section2] = x[section2] - 2 * section_length
+    output[section3] = 4 * section_length - x[section3]
+    return output
 
 
 def sawtooth(x: numpy.ndarray, section_length: float = 0.5) -> numpy.ndarray:
 
-  section0 = x < section_length
-  section1 = (x >= section_length) & (x < 2 * section_length)
-  section2 = (x >= 2 * section_length) & (x < 3 * section_length)
-  section3 = x >= 3 * section_length
-  output = numpy.zeros_like(x)
-  output[section0] = x[section0]
-  output[section1] = x[section1] - section_length
-  output[section2] = x[section2] - 2 * section_length
-  output[section3] = x[section3] - 3 * section_length
-  return output
+    section0 = x < section_length
+    section1 = (x >= section_length) & (x < 2 * section_length)
+    section2 = (x >= 2 * section_length) & (x < 3 * section_length)
+    section3 = x >= 3 * section_length
+    output = numpy.zeros_like(x)
+    output[section0] = x[section0]
+    output[section1] = x[section1] - section_length
+    output[section2] = x[section2] - 2 * section_length
+    output[section3] = x[section3] - 3 * section_length
+    return output
 
 
 class SignalGenerator:
-  """description"""
+    """description"""
 
-  def __init__(self, *funcs: Union[Callable, Number], delta_time: float = 1.0):
-    self.reset_internal_time()
-    self.delta_time = delta_time
-    self.funcs = (0, *funcs)
+    def __init__(self, *funcs: Union[Callable, Number], delta_time: float = 1.0):
+        self.reset_internal_time()
+        self.delta_time = delta_time
+        self.funcs = (0, *funcs)
 
-  def __iter__(self):
-    self.reset_internal_time()
-    return self
+    def __iter__(self):
+        self.reset_internal_time()
+        return self
 
-  def __next__(self):
-    self.t += self.delta_time
-    return self.apply(self.t)
+    def __next__(self):
+        self.t += self.delta_time
+        return self.apply(self.t)
 
-  def apply(self, t: float) -> float:
-    """
+    def apply(self, t: float) -> float:
+        """
 
-    :param t:
-    :type t:
-    :return:
-    :rtype:
-    """
-    return reduce(lambda x, y: x + y(t), self.funcs)
+        :param t:
+        :type t:
+        :return:
+        :rtype:
+        """
+        return reduce(lambda x, y: x + y(t), self.funcs)
 
-  def __call__(self, t: Iterable[Number]) -> Sequence:
-    return [self.apply(i) for i in t]
+    def __call__(self, t: Iterable[Number]) -> Sequence:
+        return [self.apply(i) for i in t]
 
-  def reset_internal_time(self):
-    """description"""
-    self.t = 0.0
+    def reset_internal_time(self):
+        """description"""
+        self.t = 0.0
 
-  def set_internal_time(self, t):
-    """
+    def set_internal_time(self, t):
+        """
 
-    :param t:
-    :type t:
-    """
-    self.t = t
+        :param t:
+        :type t:
+        """
+        self.t = t
 
-  def __enter__(self):
-    self.reset_internal_time()
-    return True
+    def __enter__(self):
+        self.reset_internal_time()
+        return True
 
 
 if __name__ == "__main__":
 
-  def asidjashdya():
-    """
-    counts
-    """
-    for _, i in zip(range(10), SignalGenerator(identity)):
-      print(i)
-
+    def asidjashdya():
+        """
+        counts
+        """
+        for _, i in zip(range(10), SignalGenerator(identity)):
+            print(i)
 
-  asidjashdya()
+    asidjashdya()
diff --git a/modulation/regression/denoising/__init__.py b/modulation/regression/denoising/__init__.py
index 7c344fd..be8bfb2 100644
--- a/modulation/regression/denoising/__init__.py
+++ b/modulation/regression/denoising/__init__.py
@@ -9,4 +9,4 @@
 
 
 from .band_pass import *
-from .finger_print import *
\ No newline at end of file
+from .finger_print import *
diff --git a/modulation/regression/linear_predictive_coding.py b/modulation/regression/linear_predictive_coding.py
index 8031e4c..e970509 100644
--- a/modulation/regression/linear_predictive_coding.py
+++ b/modulation/regression/linear_predictive_coding.py
@@ -9,53 +9,53 @@
 
 import numpy
 
-__all__ =['lpc_simple']
+__all__ = ["lpc_simple"]
 
 
 def lpc_simple(y: numpy.ndarray, m: int) -> numpy.ndarray:
-  '''
-  Return m linear predictive coefficients for sequence y using Levinson-Durbin prediction algorithm
-  '''
-
-  # step 1: compute autoregression coefficients R_0, ..., R_m
-  r = [y.dot(y)]
-  if r[0] == 0:
-    return [1] + [0] * (m - 2) + [-1]
-  else:
-    for i in range(1, m + 1):
-      r = y[i:].dot(y[:-i])
-      r.append(r)
-    r = numpy.array(r)
-
-    # step 2:
-    a = numpy.array([1, -r[1] / r[0]])
-    e = r[0] + r[1] * a[1]
-    for k in range(1, m):
-      if e == 0:
-        e = 10e-17
-      alpha = -a[: k + 1].dot(r[k + 1: 0: -1]) / e
-      a = numpy.hstack([a, 0])
-      a = a + alpha * a[::-1]
-      e *= 1 - alpha ** 2
-
-    return a
+    """
+    Return m linear predictive coefficients for sequence y using Levinson-Durbin prediction algorithm
+    """
+
+    # step 1: compute autoregression coefficients R_0, ..., R_m
+    r = [y.dot(y)]
+    if r[0] == 0:
+        return [1] + [0] * (m - 2) + [-1]
+    else:
+        for i in range(1, m + 1):
+            r = y[i:].dot(y[:-i])
+            r.append(r)
+        r = numpy.array(r)
+
+        # step 2:
+        a = numpy.array([1, -r[1] / r[0]])
+        e = r[0] + r[1] * a[1]
+        for k in range(1, m):
+            if e == 0:
+                e = 10e-17
+            alpha = -a[: k + 1].dot(r[k + 1 : 0 : -1]) / e
+            a = numpy.hstack([a, 0])
+            a = a + alpha * a[::-1]
+            e *= 1 - alpha**2
+
+        return a
 
 
 if __name__ == "__main__":
 
-  def asidj():
-    """description"""
-    import librosa
-    from matplotlib import pyplot
-
-    import scipy
-
-    y, sr = librosa.load(librosa.ex("trumpet"), duration = 0.020)
-    a = librosa.lpc(y, 2)
-    b = numpy.hstack([[0], -1 * a[1:]])
-    y_hat = scipy.signal.lfilter(b, [1], y)
-    fig, ax = pyplot.subplots()
-    ax.plot(y)
-    ax.plot(y_hat, linestyle = "--")
-    ax.legend(["y", "y_hat"])
-    ax.set_title("LP Model Forward Prediction")
+    def asidj():
+        """description"""
+        import librosa
+        from matplotlib import pyplot
+
+        import scipy
+
+        y, sr = librosa.load(librosa.ex("trumpet"), duration=0.020)
+        a = librosa.lpc(y, 2)
+        b = numpy.hstack([[0], -1 * a[1:]])
+        y_hat = scipy.signal.lfilter(b, [1], y)
+        fig, ax = pyplot.subplots()
+        ax.plot(y)
+        ax.plot(y_hat, linestyle="--")
+        ax.legend(["y", "y_hat"])
+        ax.set_title("LP Model Forward Prediction")
diff --git a/modulation/regression/spectral_denoise.py b/modulation/regression/spectral_denoise.py
index 24dc54f..761064a 100644
--- a/modulation/regression/spectral_denoise.py
+++ b/modulation/regression/spectral_denoise.py
@@ -10,22 +10,23 @@
 import numpy
 
 
-__all__=['fft_denoise']
+__all__ = ["fft_denoise"]
+
 
 def fft_denoise(
     noisy_signal: numpy.ndarray, num_time_steps: int, cutoff_threshold: float = 100
-    ) -> numpy.ndarray:
-  """description"""
+) -> numpy.ndarray:
+    """description"""
 
-  noisy_signal_f_coef = numpy.fft.fft(
-      noisy_signal, num_time_steps
-      )  # Frequency coefficients in frequency space
-  power_spectral_density = (
-      noisy_signal_f_coef * numpy.conj(noisy_signal_f_coef) / num_time_steps
-  )  # PSD
+    noisy_signal_f_coef = numpy.fft.fft(
+        noisy_signal, num_time_steps
+    )  # Frequency coefficients in frequency space
+    power_spectral_density = (
+        noisy_signal_f_coef * numpy.conj(noisy_signal_f_coef) / num_time_steps
+    )  # PSD
 
-  indices = power_spectral_density > cutoff_threshold  # 1 for true, 0 for false
+    indices = power_spectral_density > cutoff_threshold  # 1 for true, 0 for false
 
-  return numpy.fft.ifft(
-      noisy_signal_f_coef * indices
-      )  # Inverse to signal space, cleaned
+    return numpy.fft.ifft(
+        noisy_signal_f_coef * indices
+    )  # Inverse to signal space, cleaned
diff --git a/modulation/signal_utilities/__init__.py b/modulation/signal_utilities/__init__.py
index 78507e0..19cd462 100644
--- a/modulation/signal_utilities/__init__.py
+++ b/modulation/signal_utilities/__init__.py
@@ -7,8 +7,8 @@
            Created on 7/8/22
            """
 
+from .audio_utilities import *
 from .chopping import *
+from .noise_generation import *
 from .segmentation import *
 from .splitting import *
-from .audio_utilities import *
-from .noise_generation import *
\ No newline at end of file
diff --git a/modulation/signal_utilities/audio_utilities/__init__.py b/modulation/signal_utilities/audio_utilities/__init__.py
index cc646db..145b4a4 100644
--- a/modulation/signal_utilities/audio_utilities/__init__.py
+++ b/modulation/signal_utilities/audio_utilities/__init__.py
@@ -8,5 +8,5 @@
            """
 
 
+from .filtering import *
 from .persistence import *
-from .filtering import *
\ No newline at end of file
diff --git a/modulation/signal_utilities/audio_utilities/filtering.py b/modulation/signal_utilities/audio_utilities/filtering.py
index 9bfd2bd..d7f6ab5 100644
--- a/modulation/signal_utilities/audio_utilities/filtering.py
+++ b/modulation/signal_utilities/audio_utilities/filtering.py
@@ -12,7 +12,16 @@
 from scipy.signal import hilbert, lfilter
 from warg import next_pow_2
 
-__all__= ['mel_scale','in_mel_scale','pre_emphasise','pre_emphasise_torch','de_emphasise',"fft_frequencies","hilbert_envelope"]
+__all__ = [
+    "mel_scale",
+    "in_mel_scale",
+    "pre_emphasise",
+    "pre_emphasise_torch",
+    "de_emphasise",
+    "fft_frequencies",
+    "hilbert_envelope",
+]
+
 
 def mel_scale(x: numpy.ndarray) -> numpy.ndarray:
     """
diff --git a/modulation/signal_utilities/audio_utilities/persistence.py b/modulation/signal_utilities/audio_utilities/persistence.py
index 220938a..f4520ed 100644
--- a/modulation/signal_utilities/audio_utilities/persistence.py
+++ b/modulation/signal_utilities/audio_utilities/persistence.py
@@ -13,7 +13,8 @@
 import numpy
 import soundfile
 
-__all__=['audio_read','audio_write']
+__all__ = ["audio_read", "audio_write"]
+
 
 def audio_read(
     path: str, norm: bool = True, start: int = 0, stop: int = None
@@ -84,5 +85,5 @@ def audio_write(
     soundfile.write(dest_path, data, fs)
 
 
-if __name__ == '__main__':
-    pass
\ No newline at end of file
+if __name__ == "__main__":
+    pass
diff --git a/modulation/signal_utilities/chopping.py b/modulation/signal_utilities/chopping.py
index 03e7d13..8b4c8fd 100644
--- a/modulation/signal_utilities/chopping.py
+++ b/modulation/signal_utilities/chopping.py
@@ -18,7 +18,7 @@
     "separate_mask_regions",
 ]
 
-from typing import Sequence, Iterable, Tuple
+from typing import Sequence, Tuple
 
 import numpy
 
diff --git a/modulation/signal_utilities/noise_generation/__init__.py b/modulation/signal_utilities/noise_generation/__init__.py
index 6f86404..0c95d06 100644
--- a/modulation/signal_utilities/noise_generation/__init__.py
+++ b/modulation/signal_utilities/noise_generation/__init__.py
@@ -7,7 +7,7 @@
            Created on 09-12-2020
            """
 
+from .additive_noise import *
 from .babble_noise import *
-from .shaped_noise import *
 from .gaussian_noise import *
-from .additive_noise import *
\ No newline at end of file
+from .shaped_noise import *
diff --git a/modulation/signal_utilities/noise_generation/additive_noise.py b/modulation/signal_utilities/noise_generation/additive_noise.py
index 04bca4c..e86c583 100644
--- a/modulation/signal_utilities/noise_generation/additive_noise.py
+++ b/modulation/signal_utilities/noise_generation/additive_noise.py
@@ -11,16 +11,16 @@
 from typing import Any
 
 import numpy
-from draugr.visualisation import progress_bar
-from warg import ensure_existence
 from draugr.numpy_utilities import root_mean_square
-
+from draugr.visualisation import progress_bar
 from scipy import signal
 from scipy.io import wavfile
+from warg import ensure_existence
 
 from modulation.signal_utilities import mask_split_non_zero_concat
 
-__all__ = ['sample_noise','compute_additive_noise_samples']
+__all__ = ["sample_noise", "compute_additive_noise_samples"]
+
 
 def sample_noise(
     noise,
diff --git a/modulation/signal_utilities/noise_generation/gaussian_noise.py b/modulation/signal_utilities/noise_generation/gaussian_noise.py
index ec22be9..2a0c61d 100644
--- a/modulation/signal_utilities/noise_generation/gaussian_noise.py
+++ b/modulation/signal_utilities/noise_generation/gaussian_noise.py
@@ -18,132 +18,133 @@
 from matplotlib import pyplot
 from numpy.fft import irfft, rfft
 
-__all__ = ['white_noise',
-           'blue_noise',
-           'brown_noise',
-           'pink_noise',
-           'violet_noise',
-           'generate_noise',
-           'GaussianNoiseTypeEnum'
-           ]
+__all__ = [
+    "white_noise",
+    "blue_noise",
+    "brown_noise",
+    "pink_noise",
+    "violet_noise",
+    "generate_noise",
+    "GaussianNoiseTypeEnum",
+]
 
 
 def white_noise(length: int, seed: int = None) -> numpy.ndarray:
-  """
-  White noise.
+    """
+    White noise.
 
-  * N: Amount of samples.
+    * N: Amount of samples.
 
-  White noise has a constant power density.
-  Its narrowband spectrum is therefore flat.
-  The power in white noise will increase by a factor of two for each octave band,
-  and therefore increases with 3 dB per octave.
+    White noise has a constant power density.
+    Its narrowband spectrum is therefore flat.
+    The power in white noise will increase by a factor of two for each octave band,
+    and therefore increases with 3 dB per octave.
 
-  """
-  if seed:
-    numpy.random.seed(seed)
-  return numpy.random.randn(length)
+    """
+    if seed:
+        numpy.random.seed(seed)
+    return numpy.random.randn(length)
 
 
 def blue_noise(length: int, seed: int = None) -> numpy.ndarray:
-  """
-  Blue noise.
+    """
+    Blue noise.
 
-  * N: Amount of samples.
+    * N: Amount of samples.
 
-  Power increases with 6 dB per octave.
-  Power density increases with 3 dB per octave.
-  """
-  x = rfft(white_noise(length, seed)) / length
+    Power increases with 6 dB per octave.
+    Power density increases with 3 dB per octave.
+    """
+    x = rfft(white_noise(length, seed)) / length
 
-  return normalise_signal(
-      irfft(x * numpy.sqrt(numpy.arange(len(x)))).real[:length]
-      )  # Filter
+    return normalise_signal(
+        irfft(x * numpy.sqrt(numpy.arange(len(x)))).real[:length]
+    )  # Filter
 
 
 def brown_noise(length: int, seed: int = None) -> numpy.ndarray:
-  """
-  Brown noise.
+    """
+    Brown noise.
 
-  * N: Amount of samples.
+    * N: Amount of samples.
 
-  Power decreases with -3 dB per octave.
-  Power density decreases with 6 dB per octave.
+    Power decreases with -3 dB per octave.
+    Power density decreases with 6 dB per octave.
 
-  """
-  x = rfft(white_noise(length, seed)) / length
-  return normalise_signal(
-      irfft(x / (numpy.arange(len(x)) + 1)).real[:length]
-      )  # Filter
+    """
+    x = rfft(white_noise(length, seed)) / length
+    return normalise_signal(
+        irfft(x / (numpy.arange(len(x)) + 1)).real[:length]
+    )  # Filter
 
 
 def pink_noise(length: int, seed: int = None) -> numpy.ndarray:
-  """
-  Pink noise.
-
-  :param length: Amount of samples.
-  :param seed: State of PRNG.
-  :type seed: :class:`numpy.random.RandomState`
-
-  Pink noise has equal power in bands that are proportionally wide.
-  Power density decreases with 3 dB per octave.
-
-  """
-  # This method uses the filter with the following coefficients.
-  # b = numpy.array([0.049922035, -0.095993537, 0.050612699, -0.004408786])
-  # a = numpy.array([1, -2.494956002, 2.017265875, -0.522189400])
-  # return lfilter(B, A, numpy.random.randn(N))
-  # Another way would be using the FFT
-  # x = numpy.random.randn(N)
-  # X = rfft(x) / N
-  sampler = get_sampler(seed)
-  uneven = length % 2
-  x = sampler.randn(length // 2 + 1 + uneven) + 1j * sampler.randn(
-      length // 2 + 1 + uneven
-      )
-  y = (
-      irfft(x / numpy.sqrt(numpy.arange(len(x)) + 1.0))
-  ).real  # +1 to avoid divide by zero
-  if uneven:
-    y = y[:-1]
-  return normalise_signal(y)
+    """
+    Pink noise.
+
+    :param length: Amount of samples.
+    :param seed: State of PRNG.
+    :type seed: :class:`numpy.random.RandomState`
+
+    Pink noise has equal power in bands that are proportionally wide.
+    Power density decreases with 3 dB per octave.
+
+    """
+    # This method uses the filter with the following coefficients.
+    # b = numpy.array([0.049922035, -0.095993537, 0.050612699, -0.004408786])
+    # a = numpy.array([1, -2.494956002, 2.017265875, -0.522189400])
+    # return lfilter(B, A, numpy.random.randn(N))
+    # Another way would be using the FFT
+    # x = numpy.random.randn(N)
+    # X = rfft(x) / N
+    sampler = get_sampler(seed)
+    uneven = length % 2
+    x = sampler.randn(length // 2 + 1 + uneven) + 1j * sampler.randn(
+        length // 2 + 1 + uneven
+    )
+    y = (
+        irfft(x / numpy.sqrt(numpy.arange(len(x)) + 1.0))
+    ).real  # +1 to avoid divide by zero
+    if uneven:
+        y = y[:-1]
+    return normalise_signal(y)
 
 
 def violet_noise(length: int, seed: int = None) -> numpy.ndarray:
-  """
-  Violet noise. Power increases with 6 dB per octave.
+    """
+    Violet noise. Power increases with 6 dB per octave.
 
-  :param length: Amount of samples.
-  :param seed: State of PRNG.
-  :type seed: :class:`numpy.random.RandomState`
+    :param length: Amount of samples.
+    :param seed: State of PRNG.
+    :type seed: :class:`numpy.random.RandomState`
 
-  Power increases with +9 dB per octave.
-  Power density increases with +6 dB per octave.
+    Power increases with +9 dB per octave.
+    Power density increases with +6 dB per octave.
 
-  """
-  sampler = get_sampler(seed)
-  uneven = length % 2
-  x = sampler.randn(length // 2 + 1 + uneven) + 1j * sampler.randn(
-      length // 2 + 1 + uneven
-      )
-  y = (irfft(x * numpy.arange(len(x)))).real  # Filter
-  if uneven:
-    y = y[:-1]
-  return normalise_signal(y)
+    """
+    sampler = get_sampler(seed)
+    uneven = length % 2
+    x = sampler.randn(length // 2 + 1 + uneven) + 1j * sampler.randn(
+        length // 2 + 1 + uneven
+    )
+    y = (irfft(x * numpy.arange(len(x)))).real  # Filter
+    if uneven:
+        y = y[:-1]
+    return normalise_signal(y)
 
 
 class GaussianNoiseTypeEnum(Enum):
-  r"""description"""
-  white = partial(
-      white_noise
-      )  # Partial to workaround interpreting as a method definition
-  brown = partial(brown_noise)
-  blue = partial(blue_noise)
-  pink = partial(pink_noise)
-  violet = partial(violet_noise)
+    r"""description"""
+    white = partial(
+        white_noise
+    )  # Partial to workaround interpreting as a method definition
+    brown = partial(brown_noise)
+    blue = partial(blue_noise)
+    pink = partial(pink_noise)
+    violet = partial(violet_noise)
 
-  def __call__(self, *args, **kwargs):
-    return self.value(*args, **kwargs)
+    def __call__(self, *args, **kwargs):
+        return self.value(*args, **kwargs)
 
 
 def generate_noise(
@@ -153,59 +154,59 @@ def generate_noise(
     noise_type: GaussianNoiseTypeEnum = GaussianNoiseTypeEnum.white,
     export_path: Path = None,
     sampling_rate: int = 16000,
-    ) -> numpy.ndarray:
-  """
-
-  :param length:
-  :type length:
-  :param seed:
-  :type seed:
-  :param noise_type:
-  :type noise_type:
-  :param export_path:
-  :type export_path:
-  :param sampling_rate:
-  :type sampling_rate:
-  :return:
-  :rtype:
-  """
-  normalised = noise_type(length, seed)
-  if export_path:
-    torchaudio.save(str(export_path), to_tensor(normalised), sampling_rate)
-  return normalised
+) -> numpy.ndarray:
+    """
+
+    :param length:
+    :type length:
+    :param seed:
+    :type seed:
+    :param noise_type:
+    :type noise_type:
+    :param export_path:
+    :type export_path:
+    :param sampling_rate:
+    :type sampling_rate:
+    :return:
+    :rtype:
+    """
+    normalised = noise_type(length, seed)
+    if export_path:
+        torchaudio.save(str(export_path), to_tensor(normalised), sampling_rate)
+    return normalised
 
 
 if __name__ == "__main__":
 
-  def asijsda():
-    """description"""
-    sampling_rate = 16000
-    length_sec = 5
-    noises = {}
-    for a in GaussianNoiseTypeEnum:
-      name = f"{a.name}_noise.wav"
-
-      noise = generate_noise(
-          sampling_rate * length_sec,
-          seed = 42,
-          noise_type = a,
-          export_path = Path("exclude") / name,
-          )
-      noises[name] = noise
-
-    channels = numpy.array(list(noises.values()))
-    channels = numpy.concatenate(
-        [channels, normalise_signal(channels.sum(0, keepdims = True))]
+    def asijsda():
+        """description"""
+        sampling_rate = 16000
+        length_sec = 5
+        noises = {}
+        for a in GaussianNoiseTypeEnum:
+            name = f"{a.name}_noise.wav"
+
+            noise = generate_noise(
+                sampling_rate * length_sec,
+                seed=42,
+                noise_type=a,
+                export_path=Path("exclude") / name,
+            )
+            noises[name] = noise
+
+        channels = numpy.array(list(noises.values()))
+        channels = numpy.concatenate(
+            [channels, normalise_signal(channels.sum(0, keepdims=True))]
         )
-    channel_names = list(noises.keys()) + ["mixed"]
-    from draugr import dissected_channel_plot
-
-    dissected_channel_plot(
-        channels,
-        channel_names = channel_names,
-        sampling_rate = sampling_rate,
-        max_resolution = 16000,
+        channel_names = list(noises.keys()) + ["mixed"]
+        from draugr import dissected_channel_plot
+
+        dissected_channel_plot(
+            channels,
+            channel_names=channel_names,
+            sampling_rate=sampling_rate,
+            max_resolution=16000,
         )
-    pyplot.show()
+        pyplot.show()
 
-    asijsda()
+        asijsda()
diff --git a/modulation/signal_utilities/noise_generation/shaped_noise.py b/modulation/signal_utilities/noise_generation/shaped_noise.py
index 7537e3f..00b9e85 100644
--- a/modulation/signal_utilities/noise_generation/shaped_noise.py
+++ b/modulation/signal_utilities/noise_generation/shaped_noise.py
@@ -20,8 +20,7 @@
 from scipy.signal import butter, filtfilt, welch
 from warg import next_pow_2
 
-
-__all__ = ["generate_speech_shaped_noise",'spectrum_like_noise']
+__all__ = ["generate_speech_shaped_noise", "spectrum_like_noise"]
 
 from modulation.signal_utilities.audio_utilities.filtering import hilbert_envelope
 
diff --git a/modulation/torch_utilities/__init__.py b/modulation/torch_utilities/__init__.py
index b3711df..5b1c024 100644
--- a/modulation/torch_utilities/__init__.py
+++ b/modulation/torch_utilities/__init__.py
@@ -7,6 +7,6 @@
            Created on 17-12-2020
            """
 
-from .padding import *
 from .collation import *
-from .torch_transforms import *
\ No newline at end of file
+from .padding import *
+from .torch_transforms import *
diff --git a/modulation/visualisation/colorise_waveform.py b/modulation/visualisation/colorise_waveform.py
index d9f4043..1717b78 100644
--- a/modulation/visualisation/colorise_waveform.py
+++ b/modulation/visualisation/colorise_waveform.py
@@ -14,8 +14,8 @@
 from matplotlib.collections import LineCollection
 from warg import Number
 
+__all__ = ["color_plot"]
 
-__all__ =['color_plot']
 
 def color_plot(
     xy: numpy.ndarray,
diff --git a/modulation/visualisation/salience/__init__.py b/modulation/visualisation/salience/__init__.py
index f4a1971..b5131d3 100644
--- a/modulation/visualisation/salience/__init__.py
+++ b/modulation/visualisation/salience/__init__.py
@@ -7,4 +7,4 @@
            Created on 8/2/22
            """
 
-from .colored import *
\ No newline at end of file
+from .colored import *
diff --git a/samples/deprecated/chirp_plot.py b/samples/deprecated/chirp_plot.py
index bf92175..50f681b 100644
--- a/samples/deprecated/chirp_plot.py
+++ b/samples/deprecated/chirp_plot.py
@@ -10,234 +10,235 @@
 """Create plots of signals generated by chirp() and sweep_poly()."""
 
 import numpy
-from modulation import PROJECT_APP_PATH
 from numpy import poly1d
 from pylab import clf, figure, grid, plot, savefig, show, subplot, title, xlabel, ylabel
 from scipy.signal.waveforms import chirp, sweep_poly
 
+from modulation import PROJECT_APP_PATH
+
 FIG_SIZE = (7.5, 3.75)
 
 
-def make_linear(f0, t1, f1, filename = None, fig_size = FIG_SIZE):
-  """
-
-  :param f0:
-  :type f0:
-  :param t1:
-  :type t1:
-  :param f1:
-  :type f1:
-  :param filename:
-  :type filename:
-  :param fig_size:
-  :type fig_size:
-  """
-  t = numpy.linspace(0, t1, 5001)
-  w = chirp(t, f0 = f0, f1 = f1, t1 = t1, method = "linear")
-
-  figure(1, figsize = fig_size)
-  clf()
-
-  subplot(2, 1, 1)
-  plot(t, w)
-  title(f"Linear Chirp, f(0)={f0:g}, f({t1:g})={f1:g}")
-
-  subplot(2, 1, 2)
-  plot(t, f0 + (f1 - f0) * t / t1, "r")
-  grid(True)
-  ylabel("Frequency (Hz)")
-  xlabel("time (sec)")
-  if filename is None:
-    show()
-  else:
-    savefig(filename)
-
-
-def make_quadratic(f0, t1, f1, filename = None, fig_size = FIG_SIZE):
-  """
-
-  :param f0:
-  :type f0:
-  :param t1:
-  :type t1:
-  :param f1:
-  :type f1:
-  :param filename:
-  :type filename:
-  :param fig_size:
-  :type fig_size:
-  """
-  t = numpy.linspace(0, t1, 5001)
-  w = chirp(t, f0 = f0, f1 = f1, t1 = t1, method = "quadratic")
-
-  figure(1, figsize = fig_size)
-  clf()
-
-  subplot(2, 1, 1)
-  plot(t, w)
-  title(f"Quadratic Chirp, f(0)={f0:g}, f({t1:g})={f1:g}")
-
-  subplot(2, 1, 2)
-  plot(t, f0 + (f1 - f0) * t ** 2 / t1 ** 2, "r")
-  grid(True)
-  ylabel("Frequency (Hz)")
-  xlabel("time (sec)")
-  if filename is None:
-    show()
-  else:
-    savefig(filename)
-
-
-def make_quadratic_v0false(f0, t1, f1, filename = None, fig_size = FIG_SIZE):
-  """
-
-  :param f0:
-  :type f0:
-  :param t1:
-  :type t1:
-  :param f1:
-  :type f1:
-  :param filename:
-  :type filename:
-  :param fig_size:
-  :type fig_size:
-  """
-  t = numpy.linspace(0, t1, 5001)
-  w = chirp(t, f0 = f0, f1 = f1, t1 = t1, method = "quadratic", vertex_zero = False)
-
-  figure(1, figsize = fig_size)
-  clf()
-
-  subplot(2, 1, 1)
-  plot(t, w)
-  title(f"Quadratic Chirp, f(0)={f0:g}, f({t1:g})={f1:g} (vertex_zero=False)")
-
-  subplot(2, 1, 2)
-  plot(t, f1 - (f1 - f0) * (t1 - t) ** 2 / t1 ** 2, "r")
-  grid(True)
-  ylabel("Frequency (Hz)")
-  xlabel("time (sec)")
-  if filename is None:
-    show()
-  else:
-    savefig(filename)
-
-
-def make_logarithmic(f0, t1, f1, filename = None, fig_size = FIG_SIZE):
-  """
-
-  :param f0:
-  :type f0:
-  :param t1:
-  :type t1:
-  :param f1:
-  :type f1:
-  :param filename:
-  :type filename:
-  :param fig_size:
-  :type fig_size:
-  """
-  t = numpy.linspace(0, t1, 5001)
-  w = chirp(t, f0 = f0, f1 = f1, t1 = t1, method = "logarithmic")
-
-  figure(1, figsize = fig_size)
-  clf()
-
-  subplot(2, 1, 1)
-  plot(t, w)
-
-  title(f"Logarithmic Chirp, f(0)={f0:g}, f({t1:g})={f1:g}")
-
-  subplot(2, 1, 2)
-  plot(t, f0 * (f1 / f0) ** (t / t1), "r")
-  # yscale('log')
-  grid(True)
-  ylabel("Frequency (Hz)")
-  xlabel("time (sec)")
-  if filename is None:
-    show()
-  else:
-    savefig(filename)
-
-
-def make_hyperbolic(f0, t1, f1, filename = None, fig_size = FIG_SIZE):
-  """
-
-  :param f0:
-  :type f0:
-  :param t1:
-  :type t1:
-  :param f1:
-  :type f1:
-  :param filename:
-  :type filename:
-  :param fig_size:
-  :type fig_size:
-  """
-  t = numpy.linspace(0, t1, 5001)
-  w = chirp(t, f0 = f0, f1 = f1, t1 = t1, method = "hyperbolic")
-
-  figure(1, figsize = fig_size)
-  clf()
-
-  subplot(2, 1, 1)
-  plot(t, w)
-
-  title(f"Hyperbolic Chirp, f(0)={f0:g}, f({t1:g})={f1:g}")
-
-  subplot(2, 1, 2)
-  plot(t, f0 * f1 * t1 / ((f0 - f1) * t + f1 * t1), "r")
-  grid(True)
-  ylabel("Frequency (Hz)")
-  xlabel("time (sec)")
-  if filename is None:
-    show()
-  else:
-    savefig(filename)
-
-
-def make_sweep_poly(filename = None, fig_size = FIG_SIZE):
-  """
-
-  :param filename:
-  :type filename:
-  :param fig_size:
-  :type fig_size:
-  """
-  p = poly1d([0.05, -0.75, 2.5, 5.0])
-
-  t = numpy.linspace(0, t1, 5001)
-  w = sweep_poly(t, p)
-
-  figure(1, figsize = fig_size)
-  clf()
-
-  subplot(2, 1, 1)
-  plot(t, w)
-
-  title("Sweep Poly, $f(t) = 0.05t^3 - 0.75t^2 + 2.5t + 5$")
-
-  subplot(2, 1, 2)
-  plot(t, p(t), "r")
-  grid(True)
-  ylabel("Frequency (Hz)")
-  xlabel("time (sec)")
-  if filename is None:
-    show()
-  else:
-    savefig(filename)
+def make_linear(f0, t1, f1, filename=None, fig_size=FIG_SIZE):
+    """
+
+    :param f0:
+    :type f0:
+    :param t1:
+    :type t1:
+    :param f1:
+    :type f1:
+    :param filename:
+    :type filename:
+    :param fig_size:
+    :type fig_size:
+    """
+    t = numpy.linspace(0, t1, 5001)
+    w = chirp(t, f0=f0, f1=f1, t1=t1, method="linear")
+
+    figure(1, figsize=fig_size)
+    clf()
+
+    subplot(2, 1, 1)
+    plot(t, w)
+    title(f"Linear Chirp, f(0)={f0:g}, f({t1:g})={f1:g}")
+
+    subplot(2, 1, 2)
+    plot(t, f0 + (f1 - f0) * t / t1, "r")
+    grid(True)
+    ylabel("Frequency (Hz)")
+    xlabel("time (sec)")
+    if filename is None:
+        show()
+    else:
+        savefig(filename)
+
+
+def make_quadratic(f0, t1, f1, filename=None, fig_size=FIG_SIZE):
+    """
+
+    :param f0:
+    :type f0:
+    :param t1:
+    :type t1:
+    :param f1:
+    :type f1:
+    :param filename:
+    :type filename:
+    :param fig_size:
+    :type fig_size:
+    """
+    t = numpy.linspace(0, t1, 5001)
+    w = chirp(t, f0=f0, f1=f1, t1=t1, method="quadratic")
+
+    figure(1, figsize=fig_size)
+    clf()
+
+    subplot(2, 1, 1)
+    plot(t, w)
+    title(f"Quadratic Chirp, f(0)={f0:g}, f({t1:g})={f1:g}")
+
+    subplot(2, 1, 2)
+    plot(t, f0 + (f1 - f0) * t**2 / t1**2, "r")
+    grid(True)
+    ylabel("Frequency (Hz)")
+    xlabel("time (sec)")
+    if filename is None:
+        show()
+    else:
+        savefig(filename)
+
+
+def make_quadratic_v0false(f0, t1, f1, filename=None, fig_size=FIG_SIZE):
+    """
+
+    :param f0:
+    :type f0:
+    :param t1:
+    :type t1:
+    :param f1:
+    :type f1:
+    :param filename:
+    :type filename:
+    :param fig_size:
+    :type fig_size:
+    """
+    t = numpy.linspace(0, t1, 5001)
+    w = chirp(t, f0=f0, f1=f1, t1=t1, method="quadratic", vertex_zero=False)
+
+    figure(1, figsize=fig_size)
+    clf()
+
+    subplot(2, 1, 1)
+    plot(t, w)
+    title(f"Quadratic Chirp, f(0)={f0:g}, f({t1:g})={f1:g} (vertex_zero=False)")
+
+    subplot(2, 1, 2)
+    plot(t, f1 - (f1 - f0) * (t1 - t) ** 2 / t1**2, "r")
+    grid(True)
+    ylabel("Frequency (Hz)")
+    xlabel("time (sec)")
+    if filename is None:
+        show()
+    else:
+        savefig(filename)
+
+
+def make_logarithmic(f0, t1, f1, filename=None, fig_size=FIG_SIZE):
+    """
+
+    :param f0:
+    :type f0:
+    :param t1:
+    :type t1:
+    :param f1:
+    :type f1:
+    :param filename:
+    :type filename:
+    :param fig_size:
+    :type fig_size:
+    """
+    t = numpy.linspace(0, t1, 5001)
+    w = chirp(t, f0=f0, f1=f1, t1=t1, method="logarithmic")
+
+    figure(1, figsize=fig_size)
+    clf()
+
+    subplot(2, 1, 1)
+    plot(t, w)
+
+    title(f"Logarithmic Chirp, f(0)={f0:g}, f({t1:g})={f1:g}")
+
+    subplot(2, 1, 2)
+    plot(t, f0 * (f1 / f0) ** (t / t1), "r")
+    # yscale('log')
+    grid(True)
+    ylabel("Frequency (Hz)")
+    xlabel("time (sec)")
+    if filename is None:
+        show()
+    else:
+        savefig(filename)
+
+
+def make_hyperbolic(f0, t1, f1, filename=None, fig_size=FIG_SIZE):
+    """
+
+    :param f0:
+    :type f0:
+    :param t1:
+    :type t1:
+    :param f1:
+    :type f1:
+    :param filename:
+    :type filename:
+    :param fig_size:
+    :type fig_size:
+    """
+    t = numpy.linspace(0, t1, 5001)
+    w = chirp(t, f0=f0, f1=f1, t1=t1, method="hyperbolic")
+
+    figure(1, figsize=fig_size)
+    clf()
+
+    subplot(2, 1, 1)
+    plot(t, w)
+
+    title(f"Hyperbolic Chirp, f(0)={f0:g}, f({t1:g})={f1:g}")
+
+    subplot(2, 1, 2)
+    plot(t, f0 * f1 * t1 / ((f0 - f1) * t + f1 * t1), "r")
+    grid(True)
+    ylabel("Frequency (Hz)")
+    xlabel("time (sec)")
+    if filename is None:
+        show()
+    else:
+        savefig(filename)
+
+
+def make_sweep_poly(filename=None, fig_size=FIG_SIZE):
+    """
+
+    :param filename:
+    :type filename:
+    :param fig_size:
+    :type fig_size:
+    """
+    p = poly1d([0.05, -0.75, 2.5, 5.0])
+
+    t = numpy.linspace(0, t1, 5001)
+    w = sweep_poly(t, p)
+
+    figure(1, figsize=fig_size)
+    clf()
+
+    subplot(2, 1, 1)
+    plot(t, w)
+
+    title("Sweep Poly, $f(t) = 0.05t^3 - 0.75t^2 + 2.5t + 5$")
+
+    subplot(2, 1, 2)
+    plot(t, p(t), "r")
+    grid(True)
+    ylabel("Frequency (Hz)")
+    xlabel("time (sec)")
+    if filename is None:
+        show()
+    else:
+        savefig(filename)
 
 
 if __name__ == "__main__":
-  f0 = 12.5
-  t1 = 10.0
-  f1 = 2.5
-  res_folder = PROJECT_APP_PATH.user_log
-
-  make_linear(f0, t1, f1, res_folder / "chirp_linear.png")
-  make_quadratic(f0, t1, f1, res_folder / "chirp_quadratic.png")
-  make_quadratic_v0false(f0, t1, f1, res_folder / "chirp_quadratic_v0false.png")
-  make_hyperbolic(f0, t1, f1, res_folder / "chirp_hyperbolic.png")
-  make_logarithmic(f0, t1, f1, res_folder / "chirp_logarithmic.png")
-
-  make_sweep_poly(filename = res_folder / "sweep_poly.png")
+    f0 = 12.5
+    t1 = 10.0
+    f1 = 2.5
+    res_folder = PROJECT_APP_PATH.user_log
+
+    make_linear(f0, t1, f1, res_folder / "chirp_linear.png")
+    make_quadratic(f0, t1, f1, res_folder / "chirp_quadratic.png")
+    make_quadratic_v0false(f0, t1, f1, res_folder / "chirp_quadratic_v0false.png")
+    make_hyperbolic(f0, t1, f1, res_folder / "chirp_hyperbolic.png")
+    make_logarithmic(f0, t1, f1, res_folder / "chirp_logarithmic.png")
+
+    make_sweep_poly(filename=res_folder / "sweep_poly.png")
diff --git a/samples/deprecated/signal_example.py b/samples/deprecated/signal_example.py
index 6478866..6d25574 100644
--- a/samples/deprecated/signal_example.py
+++ b/samples/deprecated/signal_example.py
@@ -12,145 +12,145 @@
 
 t = numpy.linspace(1, 100, 1000)
 x_volts = 10 * numpy.sin(t / (2 * numpy.pi))
-x_watts = x_volts ** 2
+x_watts = x_volts**2
 x_db = 10 * numpy.log10(x_watts)
 
 
 def a():
-  """description"""
-  # Signal Generation
-  # matplotlib inline
-
-  pyplot.subplot(3, 1, 1)
-  pyplot.plot(t, x_volts)
-  pyplot.title("Signal")
-  pyplot.ylabel("Voltage (V)")
-  pyplot.xlabel("Time (s)")
-  pyplot.show()
-
-  pyplot.subplot(3, 1, 2)
-  pyplot.plot(t, x_watts)
-  pyplot.title("Signal Power")
-  pyplot.ylabel("Power (W)")
-  pyplot.xlabel("Time (s)")
-  pyplot.show()
-
-  pyplot.subplot(3, 1, 3)
-  pyplot.plot(t, x_db)
-  pyplot.title("Signal Power in dB")
-  pyplot.ylabel("Power (dB)")
-  pyplot.xlabel("Time (s)")
-  pyplot.show()
+    """description"""
+    # Signal Generation
+    # matplotlib inline
+
+    pyplot.subplot(3, 1, 1)
+    pyplot.plot(t, x_volts)
+    pyplot.title("Signal")
+    pyplot.ylabel("Voltage (V)")
+    pyplot.xlabel("Time (s)")
+    pyplot.show()
+
+    pyplot.subplot(3, 1, 2)
+    pyplot.plot(t, x_watts)
+    pyplot.title("Signal Power")
+    pyplot.ylabel("Power (W)")
+    pyplot.xlabel("Time (s)")
+    pyplot.show()
+
+    pyplot.subplot(3, 1, 3)
+    pyplot.plot(t, x_db)
+    pyplot.title("Signal Power in dB")
+    pyplot.ylabel("Power (dB)")
+    pyplot.xlabel("Time (s)")
+    pyplot.show()
 
 
 def noise():
-  """description"""
-  # Adding noise using target SNR
-
-  target_snr_db = 20  # Set a target SNR
-
-  sig_avg_db = 10 * numpy.log10(
-      numpy.mean(x_watts)
-      )  # Calculate signal power and convert to dB
-
-  # Calculate noise according to [2] then convert to watts
-  noise_avg_db = sig_avg_db - target_snr_db
-  noise_avg_watts = 10 ** (noise_avg_db / 10)
-
-  mean_noise = 0  # Generate an sample of white noise
-  noise_volts = numpy.random.normal(
-      mean_noise, numpy.sqrt(noise_avg_watts), len(x_watts)
-      )
-
-  y_volts = x_volts + noise_volts  # Noise up the original signal
-
-  # Plot signal with noise
-  pyplot.subplot(2, 1, 1)
-  pyplot.plot(t, y_volts)
-  pyplot.title("Signal with noise")
-  pyplot.ylabel("Voltage (V)")
-  pyplot.xlabel("Time (s)")
-  pyplot.show()
-  # Plot in dB
-  y_watts = y_volts ** 2
-  y_db = 10 * numpy.log10(y_watts)
-  pyplot.subplot(2, 1, 2)
-  pyplot.plot(t, 10 * numpy.log10(y_volts ** 2))
-  pyplot.title("Signal with noise (dB)")
-  pyplot.ylabel("Power (dB)")
-  pyplot.xlabel("Time (s)")
-  pyplot.show()
+    """description"""
+    # Adding noise using target SNR
+
+    target_snr_db = 20  # Set a target SNR
+
+    sig_avg_db = 10 * numpy.log10(
+        numpy.mean(x_watts)
+    )  # Calculate signal power and convert to dB
+
+    # Calculate noise according to [2] then convert to watts
+    noise_avg_db = sig_avg_db - target_snr_db
+    noise_avg_watts = 10 ** (noise_avg_db / 10)
+
+    mean_noise = 0  # Generate an sample of white noise
+    noise_volts = numpy.random.normal(
+        mean_noise, numpy.sqrt(noise_avg_watts), len(x_watts)
+    )
+
+    y_volts = x_volts + noise_volts  # Noise up the original signal
+
+    # Plot signal with noise
+    pyplot.subplot(2, 1, 1)
+    pyplot.plot(t, y_volts)
+    pyplot.title("Signal with noise")
+    pyplot.ylabel("Voltage (V)")
+    pyplot.xlabel("Time (s)")
+    pyplot.show()
+    # Plot in dB
+    y_watts = y_volts**2
+    y_db = 10 * numpy.log10(y_watts)
+    pyplot.subplot(2, 1, 2)
+    pyplot.plot(t, 10 * numpy.log10(y_volts**2))
+    pyplot.title("Signal with noise (dB)")
+    pyplot.ylabel("Power (dB)")
+    pyplot.xlabel("Time (s)")
+    pyplot.show()
 
 
 def spectrogram():
-  """description"""
-  from scipy import signal
-  from matplotlib import pyplot
-  import numpy
-
-  # Generate a test signal, a 2 Vrms sine wave whose frequency is slowly modulated around 3kHz, corrupted by white noise of exponentially decreasing magnitude sampled at 10 kHz.
-
-  fs = 10e3
-  N = 1e5
-  amp = 2 * numpy.sqrt(2)
-  noise_power = 0.01 * fs / 2
-  time = numpy.arange(N) / float(fs)
-  mod = 500 * numpy.cos(2 * numpy.pi * 0.25 * time)
-  carrier = amp * numpy.sin(2 * numpy.pi * 3e3 * time + mod)
-  noise = numpy.random.normal(scale = numpy.sqrt(noise_power), size = time.shape)
-  noise *= numpy.exp(-time / 5)
-  x = carrier + noise
-
-  # Compute and plot the STFT’s magnitude.
-
-  f, t, Zxx = signal.stft(x, fs, nperseg = 1000)
-  pyplot.pcolormesh(t, f, numpy.abs(Zxx), vmin = 0, vmax = amp, shading = "gouraud")
-  pyplot.title("STFT Magnitude")
-  pyplot.ylabel("Frequency [Hz]")
-  pyplot.xlabel("Time [sec]")
-  pyplot.show()
-
-  librosa.display.specshow(
-      librosa.amplitude_to_db(Y, ref = numpy.max),
-      y_axis = "linear",
-      x_axis = "time",
-      sr = Fs,
-      hop_length = H,
-      cmap = "gray_r",
-      )
+    """description"""
+    from scipy import signal
+    from matplotlib import pyplot
+    import numpy
+
+    # Generate a test signal, a 2 Vrms sine wave whose frequency is slowly modulated around 3kHz, corrupted by white noise of exponentially decreasing magnitude sampled at 10 kHz.
+
+    fs = 10e3
+    N = 1e5
+    amp = 2 * numpy.sqrt(2)
+    noise_power = 0.01 * fs / 2
+    time = numpy.arange(N) / float(fs)
+    mod = 500 * numpy.cos(2 * numpy.pi * 0.25 * time)
+    carrier = amp * numpy.sin(2 * numpy.pi * 3e3 * time + mod)
+    noise = numpy.random.normal(scale=numpy.sqrt(noise_power), size=time.shape)
+    noise *= numpy.exp(-time / 5)
+    x = carrier + noise
+
+    # Compute and plot the STFT’s magnitude.
+
+    f, t, Zxx = signal.stft(x, fs, nperseg=1000)
+    pyplot.pcolormesh(t, f, numpy.abs(Zxx), vmin=0, vmax=amp, shading="gouraud")
+    pyplot.title("STFT Magnitude")
+    pyplot.ylabel("Frequency [Hz]")
+    pyplot.xlabel("Time [sec]")
+    pyplot.show()
+
+    librosa.display.specshow(
+        librosa.amplitude_to_db(Y, ref=numpy.max),
+        y_axis="linear",
+        x_axis="time",
+        sr=Fs,
+        hop_length=H,
+        cmap="gray_r",
+    )
 
 
 spectrogram()
 
 
 def known_noise():
-  """description"""
-  # Adding noise using a target noise power
-
-  target_noise_db = 10  # Set a target channel noise power to something very noisy
-
-  target_noise_watts = 10 ** (target_noise_db / 10)  # Convert to linear Watt units
-
-  mean_noise = 0
-  noise_volts = numpy.random.normal(
-      mean_noise, numpy.sqrt(target_noise_watts), len(x_watts)
-      )  # Generate noise samples
-
-  y_volts = x_volts + noise_volts  # Noise up the original signal (again) and plot
-
-  # Plot signal with noise
-  pyplot.subplot(2, 1, 1)
-  pyplot.plot(t, y_volts)
-  pyplot.title("Signal with noise")
-  pyplot.ylabel("Voltage (V)")
-  pyplot.xlabel("Time (s)")
-  pyplot.show()
-  # Plot in dB
-  y_watts = y_volts ** 2
-  y_db = 10 * numpy.log10(y_watts)
-  pyplot.subplot(2, 1, 2)
-  pyplot.plot(t, 10 * numpy.log10(y_volts ** 2))
-  pyplot.title("Signal with noise")
-  pyplot.ylabel("Power (dB)")
-  pyplot.xlabel("Time (s)")
-  pyplot.show()
+    """description"""
+    # Adding noise using a target noise power
+
+    target_noise_db = 10  # Set a target channel noise power to something very noisy
+
+    target_noise_watts = 10 ** (target_noise_db / 10)  # Convert to linear Watt units
+
+    mean_noise = 0
+    noise_volts = numpy.random.normal(
+        mean_noise, numpy.sqrt(target_noise_watts), len(x_watts)
+    )  # Generate noise samples
+
+    y_volts = x_volts + noise_volts  # Noise up the original signal (again) and plot
+
+    # Plot signal with noise
+    pyplot.subplot(2, 1, 1)
+    pyplot.plot(t, y_volts)
+    pyplot.title("Signal with noise")
+    pyplot.ylabel("Voltage (V)")
+    pyplot.xlabel("Time (s)")
+    pyplot.show()
+    # Plot in dB
+    y_watts = y_volts**2
+    y_db = 10 * numpy.log10(y_watts)
+    pyplot.subplot(2, 1, 2)
+    pyplot.plot(t, 10 * numpy.log10(y_volts**2))
+    pyplot.title("Signal with noise")
+    pyplot.ylabel("Power (dB)")
+    pyplot.xlabel("Time (s)")
+    pyplot.show()
diff --git a/samples/deprecated/signal_generation.py b/samples/deprecated/signal_generation.py
index 3628d28..313528b 100644
--- a/samples/deprecated/signal_generation.py
+++ b/samples/deprecated/signal_generation.py
@@ -7,190 +7,185 @@
            Created on 15-01-2021
            """
 
-from modulation.data.signal_generator import SignalGenerator
 from scipy.signal import sweep_poly
 
+from modulation.data.signal_generator import SignalGenerator
+
 if __name__ == "__main__":
 
-  def asiuhda():
-    """description"""
-    from math import sin, cos
-    from scipy.signal import square
-
-    d = 1 / 10
-    t = range(100)
-    a = SignalGenerator(
-        d,
-        sin,
-        cos,
-        lambda x: cos(x + 1),
-        lambda x: sin(x + 2),
-        lambda x: cos(x + 3),
-        lambda x: sin(x + 4),
-        square,
+    def asiuhda():
+        """description"""
+        from math import sin, cos
+        from scipy.signal import square
+
+        d = 1 / 10
+        t = range(100)
+        a = SignalGenerator(
+            d,
+            sin,
+            cos,
+            lambda x: cos(x + 1),
+            lambda x: sin(x + 2),
+            lambda x: cos(x + 3),
+            lambda x: sin(x + 4),
+            square,
         )
-    from matplotlib import pyplot
+        from matplotlib import pyplot
 
-    pyplot.plot([s for _, s in zip(t, a)])
-    pyplot.show()
+        pyplot.plot([s for _, s in zip(t, a)])
+        pyplot.show()
 
+    def asduashdsdu():
+        """
 
-  def asduashdsdu():
-    """
+        :return:
+        :rtype:
+        """
+        import numpy
+        import scipy.signal
 
-    :return:
-    :rtype:
-    """
-    import numpy
-    import scipy.signal
+        sample_rate = 44100
 
-    sample_rate = 44100
+        def sine_wave(hz, peak, n_samples=sample_rate):
+            """Compute N samples of a sine wave with given frequency and peak amplitude.
+            Defaults to one second.
+            """
+            length = sample_rate / float(hz)
+            omega = numpy.pi * 2 / length
+            xvalues = numpy.arange(int(length)) * omega
+            onecycle = peak * numpy.sin(xvalues)
+            return numpy.resize(onecycle, (n_samples,)).astype(numpy.int16)
 
-    def sine_wave(hz, peak, n_samples = sample_rate):
-      """Compute N samples of a sine wave with given frequency and peak amplitude.
-      Defaults to one second.
-      """
-      length = sample_rate / float(hz)
-      omega = numpy.pi * 2 / length
-      xvalues = numpy.arange(int(length)) * omega
-      onecycle = peak * numpy.sin(xvalues)
-      return numpy.resize(onecycle, (n_samples,)).astype(numpy.int16)
+        sum([sine_wave(440, 4096), sine_wave(880, 4096)])
 
-    sum([sine_wave(440, 4096), sine_wave(880, 4096)])
+    def asiuhda2():
+        """description"""
+        from math import sin, cos
+        from scipy.signal import square, sawtooth, gausspulse
+        import numpy
 
-
-  def asiuhda2():
-    """description"""
-    from math import sin, cos
-    from scipy.signal import square, sawtooth, gausspulse
-    import numpy
-
-    """
+        """
 chirp(t, f0, t1, f1[, method, phi, vertex_zero]) 	#Frequency-swept cosine generator.
 sweep_poly(t, poly[, phi]) 	#Frequency-swept cosine generator, with a time-dependent frequency.
 """
-    t = numpy.arange(100) / 1000
-    a = SignalGenerator(
-        lambda x: sin(2 * numpy.pi * 50 * x),
-        lambda x: cos(2 * numpy.pi * 20 * x),
+        t = numpy.arange(100) / 1000
+        a = SignalGenerator(
+            lambda x: sin(2 * numpy.pi * 50 * x),
+            lambda x: cos(2 * numpy.pi * 20 * x),
         )
-    from matplotlib import pyplot
-
-    pyplot.plot(a(t))
-    pyplot.show()
+        from matplotlib import pyplot
 
+        pyplot.plot(a(t))
+        pyplot.show()
 
-  def pygame_ex2():
-    """description"""
-    import numpy
-    import pygame
+    def pygame_ex2():
+        """description"""
+        import numpy
+        import pygame
 
-    sample_rate = 44100
-    freq = 440
+        sample_rate = 44100
+        freq = 440
 
-    pygame.mixer.init(44100, -16, 2, 512)
-    # sampling frequency, size, channels, buffer
+        pygame.mixer.init(44100, -16, 2, 512)
+        # sampling frequency, size, channels, buffer
 
-    # Sampling frequency
-    # Analog audio is recorded by sampling it 44,100 times per second,
-    # and then these samples are used to reconstruct the audio signal
-    # when playing it back.
+        # Sampling frequency
+        # Analog audio is recorded by sampling it 44,100 times per second,
+        # and then these samples are used to reconstruct the audio signal
+        # when playing it back.
 
-    # size
-    # The size argument represents how many bits are used for each
-    # audio sample. If the value is negative then signed sample
-    # values will be used.
+        # size
+        # The size argument represents how many bits are used for each
+        # audio sample. If the value is negative then signed sample
+        # values will be used.
 
-    # channels
-    # 1 = mono, 2 = stereo
+        # channels
+        # 1 = mono, 2 = stereo
 
-    # buffer
-    # The buffer argument controls the number of internal samples
-    # used in the sound mixer. It can be lowered to reduce latency,
-    # but sound dropout may occur. It can be raised to larger values
-    # to ensure playback never skips, but it will impose latency on sound playback.
+        # buffer
+        # The buffer argument controls the number of internal samples
+        # used in the sound mixer. It can be lowered to reduce latency,
+        # but sound dropout may occur. It can be raised to larger values
+        # to ensure playback never skips, but it will impose latency on sound playback.
 
-    arr = numpy.array(
-        [
-            4096 * numpy.sin(2.0 * numpy.pi * freq * x / sample_rate)
-            for x in range(0, sample_rate)
+        arr = numpy.array(
+            [
+                4096 * numpy.sin(2.0 * numpy.pi * freq * x / sample_rate)
+                for x in range(0, sample_rate)
             ]
         ).astype(numpy.int16)
-    arr2 = numpy.c_[arr, arr]
-    sound = pygame.sndarray.make_sound(arr2)
-    sound.play(-1)
-    pygame.time.delay(1000)
-    sound.stop()
-
-
-  def asijasd():
-    """description"""
-    t = numpy.linspace(0, 10, 5001)
-    w = chirp(t, f0 = 12.5, f1 = 2.5, t1 = 10, method = "linear")
-
-    p = numpy.poly1d([0.05, -0.75, 2.5, 5.0])
-    t = numpy.linspace(0, 10, 5001)
-    w = sweep_poly(t, p)
-
-
-  def pygame_ex1():
-    """
-
-    :return:
-    :rtype:
-    """
-    import pygame, pygame.sndarray
-    import numpy
-    import scipy.signal
-    from time import sleep
-
-    sample_rate = 48000
-    pygame.mixer.pre_init(sample_rate, -16, 1, 1024)
-    pygame.init()
-
-    def square_wave(hz, peak, duty_cycle = 0.5, n_samples = sample_rate):
-      """
-
-      :param hz:
-      :type hz:
-      :param peak:
-      :type peak:
-      :param duty_cycle:
-      :type duty_cycle:
-      :param n_samples:
-      :type n_samples:
-      :return:
-      :rtype:
-      """
-      t = numpy.linspace(0, 1, 500 * 440 / hz, endpoint = False)
-      wave = scipy.signal.square(2 * numpy.pi * 5 * t, duty = duty_cycle)
-      wave = numpy.resize(wave, (n_samples,))
-      return peak / 2 * wave.astype(numpy.int16)
-
-    def audio_freq(freq = 800):
-      """
-
-      :param freq:
-      :type freq:
-      """
-      global sound
-      sample_wave = square_wave(freq, 4096)
-      sound = pygame.sndarray.make_sound(sample_wave)
-
-    # TEST
-    audio_freq()
-    sound.play(-1)
-    sleep(0.5)
-    sound.stop()
-    audio_freq(1000)
-    # sleep(1)
-    sound.play(-1)
-    sleep(2)
-    sound.stop()
-    sleep(1)
-    sound.play(-1)
-    sleep(0.5)
-
-
-  # asiuhda()
-  asiuhda2()
+        arr2 = numpy.c_[arr, arr]
+        sound = pygame.sndarray.make_sound(arr2)
+        sound.play(-1)
+        pygame.time.delay(1000)
+        sound.stop()
+
+    def asijasd():
+        """description"""
+        t = numpy.linspace(0, 10, 5001)
+        w = chirp(t, f0=12.5, f1=2.5, t1=10, method="linear")
+
+        p = numpy.poly1d([0.05, -0.75, 2.5, 5.0])
+        t = numpy.linspace(0, 10, 5001)
+        w = sweep_poly(t, p)
+
+    def pygame_ex1():
+        """
+
+        :return:
+        :rtype:
+        """
+        import pygame, pygame.sndarray
+        import numpy
+        import scipy.signal
+        from time import sleep
+
+        sample_rate = 48000
+        pygame.mixer.pre_init(sample_rate, -16, 1, 1024)
+        pygame.init()
+
+        def square_wave(hz, peak, duty_cycle=0.5, n_samples=sample_rate):
+            """
+
+            :param hz:
+            :type hz:
+            :param peak:
+            :type peak:
+            :param duty_cycle:
+            :type duty_cycle:
+            :param n_samples:
+            :type n_samples:
+            :return:
+            :rtype:
+            """
+            t = numpy.linspace(0, 1, 500 * 440 / hz, endpoint=False)
+            wave = scipy.signal.square(2 * numpy.pi * 5 * t, duty=duty_cycle)
+            wave = numpy.resize(wave, (n_samples,))
+            return peak / 2 * wave.astype(numpy.int16)
+
+        def audio_freq(freq=800):
+            """
+
+            :param freq:
+            :type freq:
+            """
+            global sound
+            sample_wave = square_wave(freq, 4096)
+            sound = pygame.sndarray.make_sound(sample_wave)
+
+        # TEST
+        audio_freq()
+        sound.play(-1)
+        sleep(0.5)
+        sound.stop()
+        audio_freq(1000)
+        # sleep(1)
+        sound.play(-1)
+        sleep(2)
+        sound.stop()
+        sleep(1)
+        sound.play(-1)
+        sleep(0.5)
+
+    # asiuhda()
+    asiuhda2()
diff --git a/samples/unsorted/color_line_plot.py b/samples/unsorted/color_line_plot.py
index 3b54de7..a19627a 100644
--- a/samples/unsorted/color_line_plot.py
+++ b/samples/unsorted/color_line_plot.py
@@ -16,15 +16,15 @@
 
 
 def main():
-  """description"""
-  lens = 9999
-  data = (numpy.random.random((lens)) - 0.5).cumsum(axis = 0)
-  xy = numpy.vstack((numpy.arange(lens), data)).T
-  print(xy.shape)
+    """description"""
+    lens = 9999
+    data = (numpy.random.random((lens)) - 0.5).cumsum(axis=0)
+    xy = numpy.vstack((numpy.arange(lens), data)).T
+    print(xy.shape)
 
-  color_plot(xy)
-  pyplot.show()
+    color_plot(xy)
+    pyplot.show()
 
 
 if __name__ == "__main__":
-  main()
+    main()
diff --git a/samples/unsorted/file_plot_period.py b/samples/unsorted/file_plot_period.py
index bf18ea6..ba5f9b9 100644
--- a/samples/unsorted/file_plot_period.py
+++ b/samples/unsorted/file_plot_period.py
@@ -12,6 +12,7 @@
 import numpy
 import pyaudio
 import pylab
+
 from modulation import PROJECT_APP_PATH
 
 RATE = 44100
@@ -21,34 +22,34 @@
 
 
 def soundplot(stream):
-  """
-
-  :param stream:
-  :type stream:
-  """
-  t1 = time.time()
-  data = numpy.frombuffer(stream.read(CHUNK), dtype = numpy.int16)
-  pylab.plot(data)
-  pylab.title(i)
-  pylab.grid()
-  pylab.axis([0, len(data), -(2 ** 16) / 2, 2 ** 16 / 2])
-  pylab.show()
-  pylab.savefig(str(res_folder / "03.png"), dpi = 50)
-  pylab.close("all")
-  print(f"took {(time.time() - t1) * 1000:.02f} ms")
+    """
+
+    :param stream:
+    :type stream:
+    """
+    t1 = time.time()
+    data = numpy.frombuffer(stream.read(CHUNK), dtype=numpy.int16)
+    pylab.plot(data)
+    pylab.title(i)
+    pylab.grid()
+    pylab.axis([0, len(data), -(2**16) / 2, 2**16 / 2])
+    pylab.show()
+    pylab.savefig(str(res_folder / "03.png"), dpi=50)
+    pylab.close("all")
+    print(f"took {(time.time() - t1) * 1000:.02f} ms")
 
 
 if __name__ == "__main__":
-  p = pyaudio.PyAudio()
-  stream = p.open(
-      format = pyaudio.paInt16,
-      channels = 1,
-      rate = RATE,
-      input = True,
-      frames_per_buffer = CHUNK,
-      )
-  for i in range(int(20 * RATE / CHUNK)):  # do this for 10 seconds
-    soundplot(stream)
-  stream.stop_stream()
-  stream.close()
-  p.terminate()
+    p = pyaudio.PyAudio()
+    stream = p.open(
+        format=pyaudio.paInt16,
+        channels=1,
+        rate=RATE,
+        input=True,
+        frames_per_buffer=CHUNK,
+    )
+    for i in range(int(20 * RATE / CHUNK)):  # do this for 10 seconds
+        soundplot(stream)
+    stream.stop_stream()
+    stream.close()
+    p.terminate()
diff --git a/samples/unsorted/m5_speech_command_recognition.py b/samples/unsorted/m5_speech_command_recognition.py
index e6a94c3..dbab436 100644
--- a/samples/unsorted/m5_speech_command_recognition.py
+++ b/samples/unsorted/m5_speech_command_recognition.py
@@ -14,14 +14,15 @@
 import torchaudio
 from draugr.numpy_utilities import SplitEnum
 from draugr.torch_utilities import (
-  find_n_misclassified,
-  trainable_parameters,
-  )
+    find_n_misclassified,
+    trainable_parameters,
+)
 from draugr.visualisation import progress_bar
+
 from modulation.classification.procedure import (
-  single_epoch_evaluation,
-  single_epoch_fitting,
-  )
+    single_epoch_evaluation,
+    single_epoch_fitting,
+)
 from modulation.data.audio.speech.recognition import SpeechCommands
 
 torchaudio.USE_SOUNDFILE_LEGACY_INTERFACE = False
@@ -30,13 +31,13 @@
 from torch.utils.data import DataLoader
 
 from draugr.torch_utilities import (
-  TensorBoardPytorchWriter,
-  get_model_hash,
-  get_num_parameters,
-  global_torch_device,
-  load_model,
-  save_model,
-  )
+    TensorBoardPytorchWriter,
+    get_model_hash,
+    get_num_parameters,
+    global_torch_device,
+    load_model,
+    save_model,
+)
 from draugr.random_utilities import seed_stack
 from modulation import PROJECT_APP_PATH
 from modulation.classification import M5
@@ -44,107 +45,107 @@
 
 
 def main(
-    n_epoch = 60,
-    batch_size = 256,
-    new_sample_rate = 8000,
+    n_epoch=60,
+    batch_size=256,
+    new_sample_rate=8000,
     train: bool = True,
     load_previous_model: bool = True,
-    model_name = "m5_speech_command",
-    model_storage_path = PROJECT_APP_PATH.user_data / "speech_command" / "models",
-    data_path = Path.home() / "Data" / "Audio" / "SpeechCommands",
-    drop_last_train = False,
-    ):
-  """
-
-  :param n_epoch:
-  :type n_epoch:
-  :param batch_size:
-  :type batch_size:
-  :param new_sample_rate:
-  :type new_sample_rate:
-  :param train:
-  :type train:
-  :param load_previous_model:
-  :type load_previous_model:
-  :param model_name:
-  :type model_name:
-  :param model_storage_path:
-  :type model_storage_path:
-  :param data_path:
-  :type data_path:
-  :param drop_last_train:
-  :type drop_last_train:
-  """
-  device = global_torch_device(True)
-
-  if device == "cuda":
-    num_workers = 1
-    pin_memory = True
-  else:
-    num_workers = 0
-    pin_memory = False
-
-  print(f"using {device}")
-  seed_stack(0)
-
-  train_set = SpeechCommands(data_path, split = SplitEnum.training)
-
-  waveform, sample_rate, category, speaker_id, utterance_number = train_set[0]
-  transform = torch.nn.Sequential(
-      torchaudio.transforms.Resample(orig_freq = sample_rate, new_freq = new_sample_rate)
-      )
-
-  model = M5(n_input = transform(waveform).shape[0], n_output = len(train_set.categories))
-
-  persistence_model_name = (
-      f"{model_name}_{get_model_hash(transform)}_{get_model_hash(model)}"
-  )
-  if load_previous_model:
-    candidate = load_model(
-        model_name = persistence_model_name,
-        model_directory = model_storage_path,
-        raise_on_failure = False,
+    model_name="m5_speech_command",
+    model_storage_path=PROJECT_APP_PATH.user_data / "speech_command" / "models",
+    data_path=Path.home() / "Data" / "Audio" / "SpeechCommands",
+    drop_last_train=False,
+):
+    """
+
+    :param n_epoch:
+    :type n_epoch:
+    :param batch_size:
+    :type batch_size:
+    :param new_sample_rate:
+    :type new_sample_rate:
+    :param train:
+    :type train:
+    :param load_previous_model:
+    :type load_previous_model:
+    :param model_name:
+    :type model_name:
+    :param model_storage_path:
+    :type model_storage_path:
+    :param data_path:
+    :type data_path:
+    :param drop_last_train:
+    :type drop_last_train:
+    """
+    device = global_torch_device(True)
+
+    if device == "cuda":
+        num_workers = 1
+        pin_memory = True
+    else:
+        num_workers = 0
+        pin_memory = False
+
+    print(f"using {device}")
+    seed_stack(0)
+
+    train_set = SpeechCommands(data_path, split=SplitEnum.training)
+
+    waveform, sample_rate, category, speaker_id, utterance_number = train_set[0]
+    transform = torch.nn.Sequential(
+        torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=new_sample_rate)
+    )
+
+    model = M5(n_input=transform(waveform).shape[0], n_output=len(train_set.categories))
+
+    persistence_model_name = (
+        f"{model_name}_{get_model_hash(transform)}_{get_model_hash(model)}"
+    )
+    if load_previous_model:
+        candidate = load_model(
+            model_name=persistence_model_name,
+            model_directory=model_storage_path,
+            raise_on_failure=False,
         )
-    if candidate:
-      model = candidate
-      print(f"model loaded")
+        if candidate:
+            model = candidate
+            print(f"model loaded")
 
-  transform.to(device)
-  model.to(device)
+    transform.to(device)
+    model.to(device)
 
-  collate_fn = collate_transform_wrapped(train_set.label_to_index, transform)
+    collate_fn = collate_transform_wrapped(train_set.label_to_index, transform)
 
-  # print(f"Trainable parameters: {named_trainable_parameters(model)}")
-  print(
-      f"Number of trainable parameters: {get_num_parameters(model, only_trainable = True)}"
-      )
+    # print(f"Trainable parameters: {named_trainable_parameters(model)}")
+    print(
+        f"Number of trainable parameters: {get_num_parameters(model, only_trainable = True)}"
+    )
 
-  if train:
-    train_proc(
-        model,
-        collate_fn,
-        train_set,
-        n_epoch,
-        persistence_model_name,
-        drop_last_train,
-        model_storage_path,
-        batch_size,
-        model_name,
-        num_workers,
-        pin_memory,
-        device,
-        data_path,
+    if train:
+        train_proc(
+            model,
+            collate_fn,
+            train_set,
+            n_epoch,
+            persistence_model_name,
+            drop_last_train,
+            model_storage_path,
+            batch_size,
+            model_name,
+            num_workers,
+            pin_memory,
+            device,
+            data_path,
         )
-  else:
-    stest_proc(
-        model,
-        collate_fn,
-        train_set,
-        model_name,
-        num_workers,
-        pin_memory,
-        device,
-        data_path,
+    else:
+        stest_proc(
+            model,
+            collate_fn,
+            train_set,
+            model_name,
+            num_workers,
+            pin_memory,
+            device,
+            data_path,
         )
 
 
@@ -162,141 +163,141 @@ def train_proc(
     pin_memory,
     device,
     data_path,
-    ):
-  """
-
-  :param model:
-  :type model:
-  :param collate_fn:
-  :type collate_fn:
-  :param train_set:
-  :type train_set:
-  :param n_epoch:
-  :type n_epoch:
-  :param persistence_model_name:
-  :type persistence_model_name:
-  :param drop_last_train:
-  :type drop_last_train:
-  :param model_storage_path:
-  :type model_storage_path:
-  :param batch_size:
-  :type batch_size:
-  :param model_name:
-  :type model_name:
-  :param num_workers:
-  :type num_workers:
-  :param pin_memory:
-  :type pin_memory:
-  :param device:
-  :type device:
-  :param data_path:
-  :type data_path:
-  """
-  train_loader = DataLoader(
-      train_set,
-      batch_size = batch_size,
-      shuffle = True,
-      drop_last = drop_last_train,
-      collate_fn = collate_fn,
-      num_workers = num_workers,
-      pin_memory = pin_memory,
-      )
-  valid_loader = DataLoader(
-      SpeechCommands(data_path, split = SplitEnum.validation),
-      batch_size = batch_size,
-      shuffle = False,
-      drop_last = False,
-      collate_fn = collate_fn,
-      num_workers = num_workers,
-      pin_memory = pin_memory,
-      )
-
-  optimiser = optim.Adam(trainable_parameters(model), lr = 0.01, weight_decay = 0.0001)
-  scheduler = optim.lr_scheduler.StepLR(
-      optimiser, step_size = 20, gamma = 0.1
-      )  # reduce the learning after 20 epochs by a factor of 10
-
-  with TensorBoardPytorchWriter(
-      PROJECT_APP_PATH.user_log / model_name / str(time.time())
-      ) as writer:
-    best_valid_acc = 0
-    for ith_epoch in progress_bar(
-        range(1, n_epoch + 1), total = n_epoch, description = "Epoch #"
+):
+    """
+
+    :param model:
+    :type model:
+    :param collate_fn:
+    :type collate_fn:
+    :param train_set:
+    :type train_set:
+    :param n_epoch:
+    :type n_epoch:
+    :param persistence_model_name:
+    :type persistence_model_name:
+    :param drop_last_train:
+    :type drop_last_train:
+    :param model_storage_path:
+    :type model_storage_path:
+    :param batch_size:
+    :type batch_size:
+    :param model_name:
+    :type model_name:
+    :param num_workers:
+    :type num_workers:
+    :param pin_memory:
+    :type pin_memory:
+    :param device:
+    :type device:
+    :param data_path:
+    :type data_path:
+    """
+    train_loader = DataLoader(
+        train_set,
+        batch_size=batch_size,
+        shuffle=True,
+        drop_last=drop_last_train,
+        collate_fn=collate_fn,
+        num_workers=num_workers,
+        pin_memory=pin_memory,
+    )
+    valid_loader = DataLoader(
+        SpeechCommands(data_path, split=SplitEnum.validation),
+        batch_size=batch_size,
+        shuffle=False,
+        drop_last=False,
+        collate_fn=collate_fn,
+        num_workers=num_workers,
+        pin_memory=pin_memory,
+    )
+
+    optimiser = optim.Adam(trainable_parameters(model), lr=0.01, weight_decay=0.0001)
+    scheduler = optim.lr_scheduler.StepLR(
+        optimiser, step_size=20, gamma=0.1
+    )  # reduce the learning after 20 epochs by a factor of 10
+
+    with TensorBoardPytorchWriter(
+        PROJECT_APP_PATH.user_log / model_name / str(time.time())
+    ) as writer:
+        best_valid_acc = 0
+        for ith_epoch in progress_bar(
+            range(1, n_epoch + 1), total=n_epoch, description="Epoch #"
         ):
-      single_epoch_fitting(
-          model,
-          optimiser,
-          train_loader,
-          epoch = ith_epoch,
-          writer = writer,
-          device_ = device,
-          )
-      scheduler.step()
-      accuracy = single_epoch_evaluation(
-          model,
-          valid_loader,
-          subset = SplitEnum.validation,
-          epoch = ith_epoch,
-          writer = writer,
-          device = device,
-          )
-      if accuracy > best_valid_acc:
-        best_valid_acc = accuracy
-        save_model(
-            model,
-            model_name = persistence_model_name,
-            save_directory = model_storage_path,
+            single_epoch_fitting(
+                model,
+                optimiser,
+                train_loader,
+                epoch=ith_epoch,
+                writer=writer,
+                device_=device,
+            )
+            scheduler.step()
+            accuracy = single_epoch_evaluation(
+                model,
+                valid_loader,
+                subset=SplitEnum.validation,
+                epoch=ith_epoch,
+                writer=writer,
+                device=device,
             )
-        writer.blip("new_best_model", ith_epoch)
+            if accuracy > best_valid_acc:
+                best_valid_acc = accuracy
+                save_model(
+                    model,
+                    model_name=persistence_model_name,
+                    save_directory=model_storage_path,
+                )
+                writer.blip("new_best_model", ith_epoch)
 
 
 def stest_proc(
     model, collate_fn, train_set, model_name, num_workers, pin_memory, device, data_path
-    ):
-  """
-
-  :param model:
-  :type model:
-  :param collate_fn:
-  :type collate_fn:
-  :param train_set:
-  :type train_set:
-  :param model_name:
-  :type model_name:
-  :param num_workers:
-  :type num_workers:
-  :param pin_memory:
-  :type pin_memory:
-  :param device:
-  :type device:
-  :param data_path:
-  :type data_path:
-  """
-  seed_stack(0)
-  with TensorBoardPytorchWriter(
-      PROJECT_APP_PATH.user_log / model_name / str(time.time())
-      ) as writer:
-    test_loader = DataLoader(
-        SpeechCommands(data_path, split = SplitEnum.testing),
-        batch_size = 1,
-        shuffle = True,
-        drop_last = False,
-        collate_fn = collate_fn,
-        num_workers = num_workers,
-        pin_memory = pin_memory,
+):
+    """
+
+    :param model:
+    :type model:
+    :param collate_fn:
+    :type collate_fn:
+    :param train_set:
+    :type train_set:
+    :param model_name:
+    :type model_name:
+    :param num_workers:
+    :type num_workers:
+    :param pin_memory:
+    :type pin_memory:
+    :param device:
+    :type device:
+    :param data_path:
+    :type data_path:
+    """
+    seed_stack(0)
+    with TensorBoardPytorchWriter(
+        PROJECT_APP_PATH.user_log / model_name / str(time.time())
+    ) as writer:
+        test_loader = DataLoader(
+            SpeechCommands(data_path, split=SplitEnum.testing),
+            batch_size=1,
+            shuffle=True,
+            drop_last=False,
+            collate_fn=collate_fn,
+            num_workers=num_workers,
+            pin_memory=pin_memory,
         )
-    acc = single_epoch_evaluation(
-        model,
-        test_loader,
-        subset = SplitEnum.testing,
-        epoch = 0,
-        writer = writer,
-        device = device,
+        acc = single_epoch_evaluation(
+            model,
+            test_loader,
+            subset=SplitEnum.testing,
+            epoch=0,
+            writer=writer,
+            device=device,
         )
 
-    find_n_misclassified(model, test_loader, mapper = train_set.index_to_label)
-    print(acc)
+        find_n_misclassified(model, test_loader, mapper=train_set.index_to_label)
+        print(acc)
 
 
 if __name__ == "__main__":
-  main(train = False)
+    main(train=False)
diff --git a/samples/unsorted/mpl_plot.py b/samples/unsorted/mpl_plot.py
index 6460954..724f491 100644
--- a/samples/unsorted/mpl_plot.py
+++ b/samples/unsorted/mpl_plot.py
@@ -16,28 +16,28 @@
 CHUNK = int(RATE / 20)  # RATE / number of updates per second
 
 if __name__ == "__main__":
-  p = pyaudio.PyAudio()
-  stream = p.open(
-      format = pyaudio.paInt16,
-      channels = 1,
-      rate = RATE,
-      input = True,
-      frames_per_buffer = CHUNK,
-      )
-  window_length = int(RATE / CHUNK)
+    p = pyaudio.PyAudio()
+    stream = p.open(
+        format=pyaudio.paInt16,
+        channels=1,
+        rate=RATE,
+        input=True,
+        frames_per_buffer=CHUNK,
+    )
+    window_length = int(RATE / CHUNK)
 
-  anim = discrete_scroll_plot(
-      (
-          numpy.frombuffer(stream.read(CHUNK), dtype = numpy.int16)
-          for s in range(int(20 * window_length))
-          ),
-      window_length = window_length,
-      )
+    anim = discrete_scroll_plot(
+        (
+            numpy.frombuffer(stream.read(CHUNK), dtype=numpy.int16)
+            for s in range(int(20 * window_length))
+        ),
+        window_length=window_length,
+    )
 
-  try:
-    pyplot.show()
-  except:
-    print("Plot Closed")
-    stream.stop_stream()
-    stream.close()
-    p.terminate()
+    try:
+        pyplot.show()
+    except:
+        print("Plot Closed")
+        stream.stop_stream()
+        stream.close()
+        p.terminate()
diff --git a/samples/unsorted/phase_array.py b/samples/unsorted/phase_array.py
index 99f848d..dd0f3b6 100644
--- a/samples/unsorted/phase_array.py
+++ b/samples/unsorted/phase_array.py
@@ -17,287 +17,287 @@
 
 
 def calculate_phase_from_focus(x, y, e):
-  """
-
-  :param x:
-  :type x:
-  :param y:
-  :type y:
-  :param e:
-  :type e:
-  :return:
-  :rtype:
-  """
-  return numpy.sqrt(numpy.sum((e.r - numpy.array([x, y])) ** 2)) * (
-      2 * numpy.pi / e.lambda0
-  )
-
-
-class Emitter:
-  """description"""
-
-  def __init__(self, x, y, c, f, phi, rMax = 100, color = "tab:blue", alpha = 0.6):
-    self.r, self.c, self.f, self.rMax, self.alpha = (
-        numpy.array([x, y]),
-        c,
-        f,
-        rMax,
-        alpha,
-        )
-    self.color = color
-    self.set_up()
-    self.set_phase(phi)
-
-  def increment(self, dt):
     """
 
-    :param dt:
-    :type dt:
+    :param x:
+    :type x:
+    :param y:
+    :type y:
+    :param e:
+    :type e:
     :return:
     :rtype:
     """
-    self.t += dt
-    if self.t < self.t0:
-      return
-    for i, circle in enumerate(self.circles):
-      r = i * self.lambda0 + self.wrap(
-          self.lambda0 * self.phi / (2 * numpy.pi) + self.c * self.t, self.lambda0
-          )
-      circle.set_height(2 * r)
-      circle.set_width(2 * r)
-      circle.set_alpha(self.alpha if i < ((self.t - self.t0) / self.T) else 0)
-
-  def set_phase(self, phi):
-    """
+    return numpy.sqrt(numpy.sum((e.r - numpy.array([x, y])) ** 2)) * (
+        2 * numpy.pi / e.lambda0
+    )
 
-    :param phi:
-    :type phi:
-    """
-    self.phi = self.wrap(phi, 2 * numpy.pi)
-    self.t0 = self.T * (1 - self.phi / (2 * numpy.pi))
-    self.t = 0
 
-  def set_up(self):
+class Emitter:
     """description"""
-    self.lambda0 = self.c / self.f
-    self.T = 1.0 / self.f
-    self.N = numpy.int32(numpy.ceil(self.rMax / self.lambda0))
-    self.circles = [
-        pyplot.Circle(
-            xy = tuple(self.r),
-            fill = False,
-            lw = 2,
-            radius = 0,
-            alpha = self.alpha,
-            color = self.color,
+
+    def __init__(self, x, y, c, f, phi, rMax=100, color="tab:blue", alpha=0.6):
+        self.r, self.c, self.f, self.rMax, self.alpha = (
+            numpy.array([x, y]),
+            c,
+            f,
+            rMax,
+            alpha,
+        )
+        self.color = color
+        self.set_up()
+        self.set_phase(phi)
+
+    def increment(self, dt):
+        """
+
+        :param dt:
+        :type dt:
+        :return:
+        :rtype:
+        """
+        self.t += dt
+        if self.t < self.t0:
+            return
+        for i, circle in enumerate(self.circles):
+            r = i * self.lambda0 + self.wrap(
+                self.lambda0 * self.phi / (2 * numpy.pi) + self.c * self.t, self.lambda0
+            )
+            circle.set_height(2 * r)
+            circle.set_width(2 * r)
+            circle.set_alpha(self.alpha if i < ((self.t - self.t0) / self.T) else 0)
+
+    def set_phase(self, phi):
+        """
+
+        :param phi:
+        :type phi:
+        """
+        self.phi = self.wrap(phi, 2 * numpy.pi)
+        self.t0 = self.T * (1 - self.phi / (2 * numpy.pi))
+        self.t = 0
+
+    def set_up(self):
+        """description"""
+        self.lambda0 = self.c / self.f
+        self.T = 1.0 / self.f
+        self.N = numpy.int32(numpy.ceil(self.rMax / self.lambda0))
+        self.circles = [
+            pyplot.Circle(
+                xy=tuple(self.r),
+                fill=False,
+                lw=2,
+                radius=0,
+                alpha=self.alpha,
+                color=self.color,
             )
-        for i in range(self.N)
+            for i in range(self.N)
         ]
 
-  def wrap(self, x, x_max):
-    """
+    def wrap(self, x, x_max):
+        """
 
-    :param x:
-    :type x:
-    :param x_max:
-    :type x_max:
-    :return:
-    :rtype:
-    """
-    if x >= 0:
-      return x - numpy.floor(x / x_max) * x_max
-    if x < 0:
-      return x_max - (-x - numpy.floor(-x / x_max) * x_max)
+        :param x:
+        :type x:
+        :param x_max:
+        :type x_max:
+        :return:
+        :rtype:
+        """
+        if x >= 0:
+            return x - numpy.floor(x / x_max) * x_max
+        if x < 0:
+            return x_max - (-x - numpy.floor(-x / x_max) * x_max)
 
 
 class EmitterArray:
-  """description"""
+    """description"""
 
-  def __init__(self):
-    self.emitters = []
+    def __init__(self):
+        self.emitters = []
 
-  def add_emitter(self, e):
-    """
+    def add_emitter(self, e):
+        """
 
-    :param e:
-    :type e:
-    """
-    self.emitters.append(e)
+        :param e:
+        :type e:
+        """
+        self.emitters.append(e)
 
-  def increment(self, dt):
-    """
+    def increment(self, dt):
+        """
 
-    :param dt:
-    :type dt:
-    """
-    for emitter in self.emitters:
-      emitter.increment(dt)
-
-  def get_circles(self):
-    """Get all the circles from all the emitters"""
-    circles = []
-    for emitter in self.emitters:
-      circles.extend(emitter.circles)
-    return circles
-
-  def remove_offset(self):
-    """Only run this one time after all emitters have been added"""
-    offsets = []
-    for emitter in self.emitters:
-      offsets.append(emitter.t0)
-    offset_min = numpy.min(offsets)
-    for emitter in self.emitters:
-      emitter.increment(offset_min)
-
-  @property
-  def circles(self):
-    """
+        :param dt:
+        :type dt:
+        """
+        for emitter in self.emitters:
+            emitter.increment(dt)
 
-    :return:
-    :rtype:
-    """
-    return self.get_circles()
+    def get_circles(self):
+        """Get all the circles from all the emitters"""
+        circles = []
+        for emitter in self.emitters:
+            circles.extend(emitter.circles)
+        return circles
 
+    def remove_offset(self):
+        """Only run this one time after all emitters have been added"""
+        offsets = []
+        for emitter in self.emitters:
+            offsets.append(emitter.t0)
+        offset_min = numpy.min(offsets)
+        for emitter in self.emitters:
+            emitter.increment(offset_min)
 
-def asd():
-  """
-
-  :return:
-  :rtype:
-  """
-  FPS = 30
-  X, Y = 100, 100
-  c, f = 3, 0.2
-  lambda0 = c / f
-
-  N = 10
-
-  emitter_array = EmitterArray()
-
-  # ########################################################
-  # # DEMO 1 - Linear Array of Emitters
-  # xs = numpy.linspace(-lambda0/4, lambda0/4, N)
-  # ys = numpy.zeros_like(xs)
-  # phi = numpy.linspace(0,numpy.pi/2,N)
-  # for i in range(N):
-  #     e = Emitter(xs[i], ys[i], c, f, phi[i])
-  #     emitter_array.AddEmitter(e)
-  # #######################################################
-
-  # ########################################################
-  # # DEMO 2 - Linear Array of Emitters
-  # r = numpy.linspace(-lambda0/4, lambda0/4, N)
-  # angle = numpy.pi/4
-  # xs = r*numpy.cos(angle)
-  # ys = r*numpy.sin(angle)
-  # phi = numpy.linspace(0 , numpy.pi/2, N)
-  # for i in range(N):
-  #     e = Emitter(xs[i], ys[i], c, f, phi[i])
-  #     emitter_array.AddEmitter(e)
-  # #######################################################
-
-  # ########################################################
-  # # DEMO 3 - Focussed Array
-  xs = numpy.linspace(-lambda0, lambda0, N)
-  ys = numpy.zeros_like(xs)
-  for i in range(N):
-    e = Emitter(xs[i], ys[i], c, f, 0)
-    phase = calculate_phase_from_focus(0, 20, e)
-    e.set_phase(phase)
-    emitter_array.add_emitter(e)
-  # #######################################################
-
-  # ########################################################
-  # # DEMO 4 - Dual Frequency Emitters
-  # xs = numpy.linspace(-lambda0/4, lambda0/4, N)
-  # ys = numpy.zeros_like(xs)
-  # phi = numpy.linspace(0,numpy.pi/2,N)
-  # for i in range(N):
-  #     e = Emitter(xs[i], ys[i], c, f, phi[i])
-  #     emitter_array.AddEmitter(e)
-
-  # for i in range(N):
-  #     e = Emitter(xs[i], ys[i], c, 0.5*f, -phi[i], color = "red")
-  #     emitter_array.AddEmitter(e)
-  # #######################################################
-
-  # # ########################################################
-  # # # DEMO 5 - Focussed Array
-  # xs = numpy.linspace(-lambda0, lambda0, N)
-  # ys = numpy.zeros_like(xs)
-  # for i in range(N):
-  #     e = Emitter(xs[i], ys[i], c, f, 0)
-  #     phase = CalculatePhaseFromFocus(0, 20, e)
-  #     e.SetPhase(phase)
-  #     emitter_array.AddEmitter(e)
-
-  # for i in range(N):
-  #     e = Emitter(xs[i], ys[i], c, 0.8*f, 0, color = "red")
-  #     phase = CalculatePhaseFromFocus(-20, 30, e)
-  #     e.SetPhase(phase)
-  #     emitter_array.AddEmitter(e)
-  # # #######################################################
-
-  # # # ########################################################
-  # # # # DEMO 6 - Focussed Array Random
-  # xs = numpy.random.uniform(-lambda0/2, lambda0/2, N)
-  # ys = numpy.random.uniform(-lambda0/2, lambda0/2, N)
-  # for i in range(N):
-  #     e = Emitter(xs[i], ys[i], c, f, 0)
-  #     phase = CalculatePhaseFromFocus(0, 20, e)
-  #     e.SetPhase(phase)
-  #     emitter_array.AddEmitter(e)
-  # # # #######################################################
-
-  # # # ########################################################
-  # # # # DEMO 7 - Focussed Array Random
-  # N = 20
-  # xs = numpy.random.uniform(-lambda0*2, lambda0*2, N)
-  # ys = numpy.random.uniform(-lambda0*2, lambda0*2, N)
-  # for i in range(N):
-  #     e = Emitter(xs[i], ys[i], c, f, 0)
-  #     phase = CalculatePhaseFromFocus(0, 20, e)
-  #     e.SetPhase(phase)
-  #     emitter_array.AddEmitter(e)
-  # # # #######################################################
-
-  emitter_array.remove_offset()
-
-  fig, ax = pyplot.subplots()
-  ax.set_xlim([-X / 2, Y / 2])
-  ax.set_ylim([-X / 2, Y / 2])
-  ax.set_aspect(1)
-  ax.grid(alpha = 0.2)
-  fig.tight_layout()
-
-  for circle in emitter_array.circles:
-    ax.add_patch(circle)
-
-  for emitter in emitter_array.emitters:
-    ax.add_patch(pyplot.Circle(tuple(emitter.r), 0.4, color = "purple"))
-
-  def init():
-    """
+    @property
+    def circles(self):
+        """
+
+        :return:
+        :rtype:
+        """
+        return self.get_circles()
 
-    :return:
-    :rtype:
-    """
-    return tuple(emitter_array.circles)
 
-  def update(frame_number):
+def asd():
     """
 
-    :param frame_number:
-    :type frame_number:
     :return:
     :rtype:
     """
-    emitter_array.increment(1 / FPS)
-    return tuple(emitter_array.circles)
-
-  ani = FuncAnimation(fig, update, init_func = init, interval = 1000 / FPS, blit = True)
-  pyplot.show()
+    FPS = 30
+    X, Y = 100, 100
+    c, f = 3, 0.2
+    lambda0 = c / f
+
+    N = 10
+
+    emitter_array = EmitterArray()
+
+    # ########################################################
+    # # DEMO 1 - Linear Array of Emitters
+    # xs = numpy.linspace(-lambda0/4, lambda0/4, N)
+    # ys = numpy.zeros_like(xs)
+    # phi = numpy.linspace(0,numpy.pi/2,N)
+    # for i in range(N):
+    #     e = Emitter(xs[i], ys[i], c, f, phi[i])
+    #     emitter_array.AddEmitter(e)
+    # #######################################################
+
+    # ########################################################
+    # # DEMO 2 - Linear Array of Emitters
+    # r = numpy.linspace(-lambda0/4, lambda0/4, N)
+    # angle = numpy.pi/4
+    # xs = r*numpy.cos(angle)
+    # ys = r*numpy.sin(angle)
+    # phi = numpy.linspace(0 , numpy.pi/2, N)
+    # for i in range(N):
+    #     e = Emitter(xs[i], ys[i], c, f, phi[i])
+    #     emitter_array.AddEmitter(e)
+    # #######################################################
+
+    # ########################################################
+    # # DEMO 3 - Focussed Array
+    xs = numpy.linspace(-lambda0, lambda0, N)
+    ys = numpy.zeros_like(xs)
+    for i in range(N):
+        e = Emitter(xs[i], ys[i], c, f, 0)
+        phase = calculate_phase_from_focus(0, 20, e)
+        e.set_phase(phase)
+        emitter_array.add_emitter(e)
+    # #######################################################
+
+    # ########################################################
+    # # DEMO 4 - Dual Frequency Emitters
+    # xs = numpy.linspace(-lambda0/4, lambda0/4, N)
+    # ys = numpy.zeros_like(xs)
+    # phi = numpy.linspace(0,numpy.pi/2,N)
+    # for i in range(N):
+    #     e = Emitter(xs[i], ys[i], c, f, phi[i])
+    #     emitter_array.AddEmitter(e)
+
+    # for i in range(N):
+    #     e = Emitter(xs[i], ys[i], c, 0.5*f, -phi[i], color = "red")
+    #     emitter_array.AddEmitter(e)
+    # #######################################################
+
+    # # ########################################################
+    # # # DEMO 5 - Focussed Array
+    # xs = numpy.linspace(-lambda0, lambda0, N)
+    # ys = numpy.zeros_like(xs)
+    # for i in range(N):
+    #     e = Emitter(xs[i], ys[i], c, f, 0)
+    #     phase = CalculatePhaseFromFocus(0, 20, e)
+    #     e.SetPhase(phase)
+    #     emitter_array.AddEmitter(e)
+
+    # for i in range(N):
+    #     e = Emitter(xs[i], ys[i], c, 0.8*f, 0, color = "red")
+    #     phase = CalculatePhaseFromFocus(-20, 30, e)
+    #     e.SetPhase(phase)
+    #     emitter_array.AddEmitter(e)
+    # # #######################################################
+
+    # # # ########################################################
+    # # # # DEMO 6 - Focussed Array Random
+    # xs = numpy.random.uniform(-lambda0/2, lambda0/2, N)
+    # ys = numpy.random.uniform(-lambda0/2, lambda0/2, N)
+    # for i in range(N):
+    #     e = Emitter(xs[i], ys[i], c, f, 0)
+    #     phase = CalculatePhaseFromFocus(0, 20, e)
+    #     e.SetPhase(phase)
+    #     emitter_array.AddEmitter(e)
+    # # # #######################################################
+
+    # # # ########################################################
+    # # # # DEMO 7 - Focussed Array Random
+    # N = 20
+    # xs = numpy.random.uniform(-lambda0*2, lambda0*2, N)
+    # ys = numpy.random.uniform(-lambda0*2, lambda0*2, N)
+    # for i in range(N):
+    #     e = Emitter(xs[i], ys[i], c, f, 0)
+    #     phase = CalculatePhaseFromFocus(0, 20, e)
+    #     e.SetPhase(phase)
+    #     emitter_array.AddEmitter(e)
+    # # # #######################################################
+
+    emitter_array.remove_offset()
+
+    fig, ax = pyplot.subplots()
+    ax.set_xlim([-X / 2, Y / 2])
+    ax.set_ylim([-X / 2, Y / 2])
+    ax.set_aspect(1)
+    ax.grid(alpha=0.2)
+    fig.tight_layout()
+
+    for circle in emitter_array.circles:
+        ax.add_patch(circle)
+
+    for emitter in emitter_array.emitters:
+        ax.add_patch(pyplot.Circle(tuple(emitter.r), 0.4, color="purple"))
+
+    def init():
+        """
+
+        :return:
+        :rtype:
+        """
+        return tuple(emitter_array.circles)
+
+    def update(frame_number):
+        """
+
+        :param frame_number:
+        :type frame_number:
+        :return:
+        :rtype:
+        """
+        emitter_array.increment(1 / FPS)
+        return tuple(emitter_array.circles)
+
+    ani = FuncAnimation(fig, update, init_func=init, interval=1000 / FPS, blit=True)
+    pyplot.show()
 
 
 if __name__ == "__main__":
-  asd()
+    asd()
diff --git a/samples/unsorted/slope_color.py b/samples/unsorted/slope_color.py
index 9fb094e..5b1a8e8 100644
--- a/samples/unsorted/slope_color.py
+++ b/samples/unsorted/slope_color.py
@@ -16,56 +16,56 @@
 
 
 def main2():
-  """
-  Color parts of a line based on its properties, e.g., slope.
-  """
-
-  x = numpy.linspace(0, 3 * numpy.pi, 500)
-  y = numpy.sin(x)
-  z = numpy.cos(0.5 * (x[:-1] + x[1:]))  # first derivative
-
-  # Create a colormap for red, green and blue and a norm to color
-  # f' < -0.5 red, f' > 0.5 blue, and the rest green
-  cmap = ListedColormap(["r", "g", "b"])
-  norm = BoundaryNorm([-1, -0.5, 0.5, 1], cmap.N)
-
-  # Create a set of line segments so that we can color them individually
-  # This creates the points as a N x 1 x 2 array so that we can stack points
-  # together easily to get the segments. The segments array for line collection
-  # needs to be numlines x points per line x 2 (x and y)
-  points = numpy.array([x, y]).T.reshape(-1, 1, 2)
-  segments = numpy.concatenate([points[:-1], points[1:]], axis = 1)
-
-  # Create the line collection object, setting the colormapping parameters.
-  # Have to set the actual values used for colormapping separately.
-  lc = LineCollection(segments, cmap = cmap, norm = norm)
-  lc.set_array(z)
-  lc.set_linewidth(3)
-
-  fig1 = pyplot.figure()
-  pyplot.gca().add_collection(lc)
-  pyplot.xlim(x.min(), x.max())
-  pyplot.ylim(-1.1, 1.1)
-
-  # Now do a second plot coloring the curve using a continuous colormap
-  t = numpy.linspace(0, 10, 200)
-  x = numpy.cos(numpy.pi * t)
-  y = numpy.sin(t)
-  points = numpy.array([x, y]).T.reshape(-1, 1, 2)
-  segments = numpy.concatenate([points[:-1], points[1:]], axis = 1)
-
-  lc = LineCollection(
-      segments, cmap = pyplot.get_cmap("copper"), norm = pyplot.Normalize(0, 10)
-      )
-  lc.set_array(t)
-  lc.set_linewidth(3)
-
-  fig2 = pyplot.figure()
-  pyplot.gca().add_collection(lc)
-  pyplot.xlim(-1, 1)
-  pyplot.ylim(-1, 1)
-  pyplot.show()
+    """
+    Color parts of a line based on its properties, e.g., slope.
+    """
+
+    x = numpy.linspace(0, 3 * numpy.pi, 500)
+    y = numpy.sin(x)
+    z = numpy.cos(0.5 * (x[:-1] + x[1:]))  # first derivative
+
+    # Create a colormap for red, green and blue and a norm to color
+    # f' < -0.5 red, f' > 0.5 blue, and the rest green
+    cmap = ListedColormap(["r", "g", "b"])
+    norm = BoundaryNorm([-1, -0.5, 0.5, 1], cmap.N)
+
+    # Create a set of line segments so that we can color them individually
+    # This creates the points as a N x 1 x 2 array so that we can stack points
+    # together easily to get the segments. The segments array for line collection
+    # needs to be numlines x points per line x 2 (x and y)
+    points = numpy.array([x, y]).T.reshape(-1, 1, 2)
+    segments = numpy.concatenate([points[:-1], points[1:]], axis=1)
+
+    # Create the line collection object, setting the colormapping parameters.
+    # Have to set the actual values used for colormapping separately.
+    lc = LineCollection(segments, cmap=cmap, norm=norm)
+    lc.set_array(z)
+    lc.set_linewidth(3)
+
+    fig1 = pyplot.figure()
+    pyplot.gca().add_collection(lc)
+    pyplot.xlim(x.min(), x.max())
+    pyplot.ylim(-1.1, 1.1)
+
+    # Now do a second plot coloring the curve using a continuous colormap
+    t = numpy.linspace(0, 10, 200)
+    x = numpy.cos(numpy.pi * t)
+    y = numpy.sin(t)
+    points = numpy.array([x, y]).T.reshape(-1, 1, 2)
+    segments = numpy.concatenate([points[:-1], points[1:]], axis=1)
+
+    lc = LineCollection(
+        segments, cmap=pyplot.get_cmap("copper"), norm=pyplot.Normalize(0, 10)
+    )
+    lc.set_array(t)
+    lc.set_linewidth(3)
+
+    fig2 = pyplot.figure()
+    pyplot.gca().add_collection(lc)
+    pyplot.xlim(-1, 1)
+    pyplot.ylim(-1, 1)
+    pyplot.show()
 
 
 if __name__ == "__main__":
-  main2()
+    main2()
diff --git a/samples/unsorted/term_plot.py b/samples/unsorted/term_plot.py
index 9379664..0981956 100644
--- a/samples/unsorted/term_plot.py
+++ b/samples/unsorted/term_plot.py
@@ -15,18 +15,18 @@
 CHUNK = int(RATE / 20)  # RATE / number of updates per second
 
 if __name__ == "__main__":
-  p = pyaudio.PyAudio()
-  stream = p.open(
-      format = pyaudio.paInt16,
-      channels = 1,
-      rate = RATE,
-      input = True,
-      frames_per_buffer = CHUNK,
-      )
-  window_length = int(RATE / CHUNK)
-  a = list(
-      numpy.frombuffer(stream.read(CHUNK), dtype = numpy.int16)
-      for s in range(int(1 * window_length))
-      )
-  print(a)
-  terminal_plot(a[-1])
+    p = pyaudio.PyAudio()
+    stream = p.open(
+        format=pyaudio.paInt16,
+        channels=1,
+        rate=RATE,
+        input=True,
+        frames_per_buffer=CHUNK,
+    )
+    window_length = int(RATE / CHUNK)
+    a = list(
+        numpy.frombuffer(stream.read(CHUNK), dtype=numpy.int16)
+        for s in range(int(1 * window_length))
+    )
+    print(a)
+    terminal_plot(a[-1])
diff --git a/samples/unsorted/vad_stests/mic_vad_streaming.py b/samples/unsorted/vad_stests/mic_vad_streaming.py
index a8d42e6..a8028f9 100644
--- a/samples/unsorted/vad_stests/mic_vad_streaming.py
+++ b/samples/unsorted/vad_stests/mic_vad_streaming.py
@@ -37,299 +37,299 @@
 
 """
 
-logging.basicConfig(level = 20)
+logging.basicConfig(level=20)
 
 
 class Audio(object):
-  """Streams raw audio from microphone. Data is received in a separate thread, and stored in a buffer, to be read from."""
-
-  FORMAT = pyaudio.paInt16
-  RATE_PROCESS = 16000  # Network/VAD rate-space
-  CHANNELS = 1
-  BLOCKS_PER_SECOND = 50
-
-  def __init__(self, callback = None, device = None, input_rate = RATE_PROCESS, file = None):
-    def proxy_callback(in_data, frame_count, time_info, status):
-      """
-
-      :param in_data:
-      :type in_data:
-      :param frame_count:
-      :type frame_count:
-      :param time_info:
-      :type time_info:
-      :param status:
-      :type status:
-      :return:
-      :rtype:
-      """
-      # pylint: disable=unused-argument
-      if self.chunk is not None:
-        in_data = self.wf.readframes(self.chunk)
-      callback(in_data)
-      return None, pyaudio.paContinue
-
-    if callback is None:
-      callback = lambda in_data: self.buffer_queue.put(in_data)
-    self.buffer_queue = queue.Queue()
-    self.device = device
-    self.input_rate = input_rate
-    self.sample_rate = self.RATE_PROCESS
-    self.block_size = int(self.RATE_PROCESS / float(self.BLOCKS_PER_SECOND))
-    self.block_size_input = int(self.input_rate / float(self.BLOCKS_PER_SECOND))
-    self.pa = pyaudio.PyAudio()
-
-    kwargs = {
-        "format":            self.FORMAT,
-        "channels":          self.CHANNELS,
-        "rate":              self.input_rate,
-        "input":             True,
-        "frames_per_buffer": self.block_size_input,
-        "stream_callback":   proxy_callback,
+    """Streams raw audio from microphone. Data is received in a separate thread, and stored in a buffer, to be read from."""
+
+    FORMAT = pyaudio.paInt16
+    RATE_PROCESS = 16000  # Network/VAD rate-space
+    CHANNELS = 1
+    BLOCKS_PER_SECOND = 50
+
+    def __init__(self, callback=None, device=None, input_rate=RATE_PROCESS, file=None):
+        def proxy_callback(in_data, frame_count, time_info, status):
+            """
+
+            :param in_data:
+            :type in_data:
+            :param frame_count:
+            :type frame_count:
+            :param time_info:
+            :type time_info:
+            :param status:
+            :type status:
+            :return:
+            :rtype:
+            """
+            # pylint: disable=unused-argument
+            if self.chunk is not None:
+                in_data = self.wf.readframes(self.chunk)
+            callback(in_data)
+            return None, pyaudio.paContinue
+
+        if callback is None:
+            callback = lambda in_data: self.buffer_queue.put(in_data)
+        self.buffer_queue = queue.Queue()
+        self.device = device
+        self.input_rate = input_rate
+        self.sample_rate = self.RATE_PROCESS
+        self.block_size = int(self.RATE_PROCESS / float(self.BLOCKS_PER_SECOND))
+        self.block_size_input = int(self.input_rate / float(self.BLOCKS_PER_SECOND))
+        self.pa = pyaudio.PyAudio()
+
+        kwargs = {
+            "format": self.FORMAT,
+            "channels": self.CHANNELS,
+            "rate": self.input_rate,
+            "input": True,
+            "frames_per_buffer": self.block_size_input,
+            "stream_callback": proxy_callback,
         }
 
-    self.chunk = None
-    # if not default device
-    if self.device:
-      kwargs["input_device_index"] = self.device
-    elif file is not None:
-      self.chunk = 320
-      self.wf = wave.open(file, "rb")
+        self.chunk = None
+        # if not default device
+        if self.device:
+            kwargs["input_device_index"] = self.device
+        elif file is not None:
+            self.chunk = 320
+            self.wf = wave.open(file, "rb")
+
+        self.stream = self.pa.open(**kwargs)
+        self.stream.start_stream()
+
+    def resample(self, data, input_rate):
+        """
+        Microphone may not support our native processing sampling rate, so
+        resample from input_rate to RATE_PROCESS here for webrtcvad and
+        deepspeech
+
+        Args:
+            data (binary): Input audio stream
+            input_rate (int): Input audio rate to resample from
+        """
+        data16 = numpy.fromstring(string=data, dtype=numpy.int16)
+        resample_size = int(len(data16) / self.input_rate * self.RATE_PROCESS)
+        resample = signal.resample(data16, resample_size)
+        resample16 = numpy.array(resample, dtype=numpy.int16)
+        return resample16.tostring()
+
+    def read_resampled(self):
+        """Return a block of audio data resampled to 16000hz, blocking if necessary."""
+        return self.resample(data=self.buffer_queue.get(), input_rate=self.input_rate)
+
+    def read(self):
+        """Return a block of audio data, blocking if necessary."""
+        return self.buffer_queue.get()
+
+    def destroy(self):
+        """description"""
+        self.stream.stop_stream()
+        self.stream.close()
+        self.pa.terminate()
+
+    frame_duration_ms = property(
+        lambda self: 1000 * self.block_size // self.sample_rate
+    )
+
+    def write_wav(self, filename, data):
+        """
+
+        :param filename:
+        :type filename:
+        :param data:
+        :type data:
+        """
+        logging.info("write wav %s", filename)
+        wf = wave.open(filename, "wb")
+        wf.setnchannels(self.CHANNELS)
+        # wf.setsampwidth(self.pa.get_sample_size(FORMAT))
+        assert self.FORMAT == pyaudio.paInt16
+        wf.setsampwidth(2)
+        wf.setframerate(self.sample_rate)
+        wf.writeframes(data)
+        wf.close()
 
-    self.stream = self.pa.open(**kwargs)
-    self.stream.start_stream()
 
-  def resample(self, data, input_rate):
-    """
-    Microphone may not support our native processing sampling rate, so
-    resample from input_rate to RATE_PROCESS here for webrtcvad and
-    deepspeech
+class VADAudio(Audio):
+    """Filter & segment audio with voice activity detection."""
+
+    def __init__(self, aggressiveness=3, device=None, input_rate=None, file=None):
+        super().__init__(device=device, input_rate=input_rate, file=file)
+        self.vad = webrtcvad.Vad(aggressiveness)
+
+    def frame_generator(self):
+        """Generator that yields all audio frames from microphone."""
+        if self.input_rate == self.RATE_PROCESS:
+            while True:
+                yield self.read()
+        else:
+            while True:
+                yield self.read_resampled()
+
+    def vad_collector(self, padding_ms=300, ratio=0.75, frames=None):
+        """Generator that yields series of consecutive audio frames comprising each utterence, separated by yielding a single None.
+        Determines voice activity by ratio of frames in padding_ms. Uses a buffer to include padding_ms prior to being triggered.
+        Example: (frame, ..., frame, None, frame, ..., frame, None, ...)
+                  |---utterence---|        |---utterence---|
+        """
+        if frames is None:
+            frames = self.frame_generator()
+        num_padding_frames = padding_ms // self.frame_duration_ms
+        ring_buffer = collections.deque(maxlen=num_padding_frames)
+        triggered = False
+
+        for frame in frames:
+            if len(frame) < 640:
+                return
+
+            is_speech = self.vad.is_speech(frame, self.sample_rate)
+
+            if not triggered:
+                ring_buffer.append((frame, is_speech))
+                num_voiced = len([f for f, speech in ring_buffer if speech])
+                if num_voiced > ratio * ring_buffer.maxlen:
+                    triggered = True
+                    for f, s in ring_buffer:
+                        yield f
+                    ring_buffer.clear()
+
+            else:
+                yield frame
+                ring_buffer.append((frame, is_speech))
+                num_unvoiced = len([f for f, speech in ring_buffer if not speech])
+                if num_unvoiced > ratio * ring_buffer.maxlen:
+                    triggered = False
+                    yield None
+                    ring_buffer.clear()
 
-    Args:
-        data (binary): Input audio stream
-        input_rate (int): Input audio rate to resample from
-    """
-    data16 = numpy.fromstring(string = data, dtype = numpy.int16)
-    resample_size = int(len(data16) / self.input_rate * self.RATE_PROCESS)
-    resample = signal.resample(data16, resample_size)
-    resample16 = numpy.array(resample, dtype = numpy.int16)
-    return resample16.tostring()
-
-  def read_resampled(self):
-    """Return a block of audio data resampled to 16000hz, blocking if necessary."""
-    return self.resample(data = self.buffer_queue.get(), input_rate = self.input_rate)
-
-  def read(self):
-    """Return a block of audio data, blocking if necessary."""
-    return self.buffer_queue.get()
-
-  def destroy(self):
-    """description"""
-    self.stream.stop_stream()
-    self.stream.close()
-    self.pa.terminate()
-
-  frame_duration_ms = property(
-      lambda self: 1000 * self.block_size // self.sample_rate
-      )
-
-  def write_wav(self, filename, data):
-    """
 
-    :param filename:
-    :type filename:
-    :param data:
-    :type data:
+def main(ARGS):
     """
-    logging.info("write wav %s", filename)
-    wf = wave.open(filename, "wb")
-    wf.setnchannels(self.CHANNELS)
-    # wf.setsampwidth(self.pa.get_sample_size(FORMAT))
-    assert self.FORMAT == pyaudio.paInt16
-    wf.setsampwidth(2)
-    wf.setframerate(self.sample_rate)
-    wf.writeframes(data)
-    wf.close()
-
 
-class VADAudio(Audio):
-  """Filter & segment audio with voice activity detection."""
-
-  def __init__(self, aggressiveness = 3, device = None, input_rate = None, file = None):
-    super().__init__(device = device, input_rate = input_rate, file = file)
-    self.vad = webrtcvad.Vad(aggressiveness)
-
-  def frame_generator(self):
-    """Generator that yields all audio frames from microphone."""
-    if self.input_rate == self.RATE_PROCESS:
-      while True:
-        yield self.read()
-    else:
-      while True:
-        yield self.read_resampled()
-
-  def vad_collector(self, padding_ms = 300, ratio = 0.75, frames = None):
-    """Generator that yields series of consecutive audio frames comprising each utterence, separated by yielding a single None.
-    Determines voice activity by ratio of frames in padding_ms. Uses a buffer to include padding_ms prior to being triggered.
-    Example: (frame, ..., frame, None, frame, ..., frame, None, ...)
-              |---utterence---|        |---utterence---|
+    :param ARGS:
+    :type ARGS:
     """
-    if frames is None:
-      frames = self.frame_generator()
-    num_padding_frames = padding_ms // self.frame_duration_ms
-    ring_buffer = collections.deque(maxlen = num_padding_frames)
-    triggered = False
-
+    if os.path.isdir(ARGS.model):  # Load DeepSpeech model
+        model_dir = ARGS.model
+        ARGS.model = os.path.join(model_dir, "output_graph.pb")
+        ARGS.scorer = os.path.join(model_dir, ARGS.scorer)
+
+    print("Initializing model...")
+    logging.info("ARGS.model: %s", ARGS.model)
+    model = deepspeech.Model(ARGS.model)
+    if ARGS.scorer:
+        logging.info("ARGS.scorer: %s", ARGS.scorer)
+        model.enableExternalScorer(ARGS.scorer)
+
+    # Start audio with VAD
+    vad_audio = VADAudio(
+        aggressiveness=ARGS.vad_aggressiveness,
+        device=ARGS.device,
+        input_rate=ARGS.rate,
+        file=ARGS.file,
+    )
+    print("Listening (ctrl-C to exit)...")
+    frames = vad_audio.vad_collector()
+
+    # Stream from microphone to DeepSpeech using VAD
+    spinner = None
+    if not ARGS.nospinner:
+        spinner = Halo(spinner="line")
+    stream_context = model.createStream()
+    wav_data = bytearray()
     for frame in frames:
-      if len(frame) < 640:
-        return
-
-      is_speech = self.vad.is_speech(frame, self.sample_rate)
-
-      if not triggered:
-        ring_buffer.append((frame, is_speech))
-        num_voiced = len([f for f, speech in ring_buffer if speech])
-        if num_voiced > ratio * ring_buffer.maxlen:
-          triggered = True
-          for f, s in ring_buffer:
-            yield f
-          ring_buffer.clear()
-
-      else:
-        yield frame
-        ring_buffer.append((frame, is_speech))
-        num_unvoiced = len([f for f, speech in ring_buffer if not speech])
-        if num_unvoiced > ratio * ring_buffer.maxlen:
-          triggered = False
-          yield None
-          ring_buffer.clear()
-
-
-def main(ARGS):
-  """
-
-  :param ARGS:
-  :type ARGS:
-  """
-  if os.path.isdir(ARGS.model):  # Load DeepSpeech model
-    model_dir = ARGS.model
-    ARGS.model = os.path.join(model_dir, "output_graph.pb")
-    ARGS.scorer = os.path.join(model_dir, ARGS.scorer)
-
-  print("Initializing model...")
-  logging.info("ARGS.model: %s", ARGS.model)
-  model = deepspeech.Model(ARGS.model)
-  if ARGS.scorer:
-    logging.info("ARGS.scorer: %s", ARGS.scorer)
-    model.enableExternalScorer(ARGS.scorer)
-
-  # Start audio with VAD
-  vad_audio = VADAudio(
-      aggressiveness = ARGS.vad_aggressiveness,
-      device = ARGS.device,
-      input_rate = ARGS.rate,
-      file = ARGS.file,
-      )
-  print("Listening (ctrl-C to exit)...")
-  frames = vad_audio.vad_collector()
-
-  # Stream from microphone to DeepSpeech using VAD
-  spinner = None
-  if not ARGS.nospinner:
-    spinner = Halo(spinner = "line")
-  stream_context = model.createStream()
-  wav_data = bytearray()
-  for frame in frames:
-    if frame is not None:
-      if spinner:
-        spinner.start()
-      logging.debug("streaming frame")
-      stream_context.feedAudioContent(numpy.frombuffer(frame, numpy.int16))
-      if ARGS.savewav:
-        wav_data.extend(frame)
-    else:
-      if spinner:
-        spinner.stop()
-      logging.debug("end utterence")
-      if ARGS.savewav:
-        vad_audio.write_wav(
-            os.path.join(
-                ARGS.savewav,
-                datetime.now().strftime("savewav_%Y-%m-%d_%H-%M-%S_%f.wav"),
-                ),
-            wav_data,
-            )
-        wav_data = bytearray()
-      text = stream_context.finishStream()
-      print(f"Recognized: {text}")
-      if ARGS.keyboard:
-        from pyautogui import typewrite
-
-        typewrite(text)
-      stream_context = model.createStream()
+        if frame is not None:
+            if spinner:
+                spinner.start()
+            logging.debug("streaming frame")
+            stream_context.feedAudioContent(numpy.frombuffer(frame, numpy.int16))
+            if ARGS.savewav:
+                wav_data.extend(frame)
+        else:
+            if spinner:
+                spinner.stop()
+            logging.debug("end utterence")
+            if ARGS.savewav:
+                vad_audio.write_wav(
+                    os.path.join(
+                        ARGS.savewav,
+                        datetime.now().strftime("savewav_%Y-%m-%d_%H-%M-%S_%f.wav"),
+                    ),
+                    wav_data,
+                )
+                wav_data = bytearray()
+            text = stream_context.finishStream()
+            print(f"Recognized: {text}")
+            if ARGS.keyboard:
+                from pyautogui import typewrite
+
+                typewrite(text)
+            stream_context = model.createStream()
 
 
 if __name__ == "__main__":
-  DEFAULT_SAMPLE_RATE = 16000
-
-  import argparse
-
-  parser = argparse.ArgumentParser(
-      description = "Stream from microphone to DeepSpeech using VAD"
-      )
-
-  parser.add_argument(
-      "-v",
-      "--vad_aggressiveness",
-      type = int,
-      default = 3,
-      help = "Set aggressiveness of VAD: an integer between 0 and 3, 0 being the least aggressive about filtering out non-speech, 3 the most aggressive. Default: 3",
-      )
-  parser.add_argument("--nospinner", action = "store_true", help = "Disable spinner")
-  parser.add_argument(
-      "-w", "--savewav", help = "Save .wav files of utterences to given directory"
-      )
-  parser.add_argument(
-      "-f",
-      "--file",
-      # default=Path.home()/'Downloads'/'audio'/'2830-3980-0043.wav',
-      help = "Read from .wav file instead of microphone",
-      )
-
-  parser.add_argument(
-      "-m",
-      "--model",
-      # required=True,
-      default = str(Path.home() / "Downloads" / "deepspeech-0.9.1-models.pbmm"),
-      help = "Path to the model (protocol buffer binary file, or entire directory containing all standard-named files for model)",
-      )
-  parser.add_argument(
-      "-s",
-      "--scorer",
-      default = str(Path.home() / "Downloads" / "deepspeech-0.9.1-models.scorer"),
-      help = "Path to the external scorer file.",
-      )
-  parser.add_argument(
-      "-d",
-      "--device",
-      type = int,
-      default = None,
-      help = "Device input index (Int) as listed by pyaudio.PyAudio.get_device_info_by_index(). If not provided, falls back to PyAudio.get_default_device().",
-      )
-  parser.add_argument(
-      "-r",
-      "--rate",
-      type = int,
-      default = DEFAULT_SAMPLE_RATE,
-      help = f"Input device sample rate. Default: {DEFAULT_SAMPLE_RATE}. Your device may require 44100.",
-      )
-  parser.add_argument(
-      "-k",
-      "--keyboard",
-      action = "store_true",
-      help = "Type output through system keyboard",
-      )
-  ARGS = parser.parse_args()
-  if ARGS.savewav:
-    os.makedirs(ARGS.savewav, exist_ok = True)
-  main(ARGS)
+    DEFAULT_SAMPLE_RATE = 16000
+
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description="Stream from microphone to DeepSpeech using VAD"
+    )
+
+    parser.add_argument(
+        "-v",
+        "--vad_aggressiveness",
+        type=int,
+        default=3,
+        help="Set aggressiveness of VAD: an integer between 0 and 3, 0 being the least aggressive about filtering out non-speech, 3 the most aggressive. Default: 3",
+    )
+    parser.add_argument("--nospinner", action="store_true", help="Disable spinner")
+    parser.add_argument(
+        "-w", "--savewav", help="Save .wav files of utterences to given directory"
+    )
+    parser.add_argument(
+        "-f",
+        "--file",
+        # default=Path.home()/'Downloads'/'audio'/'2830-3980-0043.wav',
+        help="Read from .wav file instead of microphone",
+    )
+
+    parser.add_argument(
+        "-m",
+        "--model",
+        # required=True,
+        default=str(Path.home() / "Downloads" / "deepspeech-0.9.1-models.pbmm"),
+        help="Path to the model (protocol buffer binary file, or entire directory containing all standard-named files for model)",
+    )
+    parser.add_argument(
+        "-s",
+        "--scorer",
+        default=str(Path.home() / "Downloads" / "deepspeech-0.9.1-models.scorer"),
+        help="Path to the external scorer file.",
+    )
+    parser.add_argument(
+        "-d",
+        "--device",
+        type=int,
+        default=None,
+        help="Device input index (Int) as listed by pyaudio.PyAudio.get_device_info_by_index(). If not provided, falls back to PyAudio.get_default_device().",
+    )
+    parser.add_argument(
+        "-r",
+        "--rate",
+        type=int,
+        default=DEFAULT_SAMPLE_RATE,
+        help=f"Input device sample rate. Default: {DEFAULT_SAMPLE_RATE}. Your device may require 44100.",
+    )
+    parser.add_argument(
+        "-k",
+        "--keyboard",
+        action="store_true",
+        help="Type output through system keyboard",
+    )
+    ARGS = parser.parse_args()
+    if ARGS.savewav:
+        os.makedirs(ARGS.savewav, exist_ok=True)
+    main(ARGS)
diff --git a/samples/unsorted/vad_stests/novad_real_time_inference.py b/samples/unsorted/vad_stests/novad_real_time_inference.py
index 0cbba52..0a4cb9b 100644
--- a/samples/unsorted/vad_stests/novad_real_time_inference.py
+++ b/samples/unsorted/vad_stests/novad_real_time_inference.py
@@ -17,16 +17,16 @@
 import keyboard
 import pyaudio
 
-parser = argparse.ArgumentParser(description = __doc__)
+parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument(
     "--host_ip",
-    default = "localhost",
-    type = str,
-    help = "Server IP address. (default: %(default)s)",
-    )
+    default="localhost",
+    type=str,
+    help="Server IP address. (default: %(default)s)",
+)
 parser.add_argument(
-    "--host_port", default = 8086, type = int, help = "Server Port. (default: %(default)s)"
-    )
+    "--host_port", default=8086, type=int, help="Server Port. (default: %(default)s)"
+)
 args = parser.parse_args()
 
 is_recording = False
@@ -34,68 +34,68 @@
 
 
 def on_press_release(x):
-  """Keyboard callback function."""
-  global is_recording, enable_trigger_record
-  press = keyboard.KeyboardEvent("down", 28, "space")
-  release = keyboard.KeyboardEvent("up", 28, "space")
-  if x.event_type == "down" and x.name == press.name:
-    if (not is_recording) and enable_trigger_record:
-      sys.stdout.write("Start Recording ... ")
-      sys.stdout.flush()
-      is_recording = True
-  if x.event_type == "up" and x.name == release.name:
-    if is_recording == True:
-      is_recording = False
+    """Keyboard callback function."""
+    global is_recording, enable_trigger_record
+    press = keyboard.KeyboardEvent("down", 28, "space")
+    release = keyboard.KeyboardEvent("up", 28, "space")
+    if x.event_type == "down" and x.name == press.name:
+        if (not is_recording) and enable_trigger_record:
+            sys.stdout.write("Start Recording ... ")
+            sys.stdout.flush()
+            is_recording = True
+    if x.event_type == "up" and x.name == release.name:
+        if is_recording == True:
+            is_recording = False
 
 
 data_list = []
 
 
 def callback(in_data, frame_count, time_info, status):
-  """Audio recorder's stream callback function."""
-  global data_list, is_recording, enable_trigger_record
-  if is_recording:
-    data_list.append(in_data)
-    enable_trigger_record = False
-  elif len(data_list) > 0:
-    # Connect to server and send data
-    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-    sock.connect((args.host_ip, args.host_port))
-    sent = "".join(data_list)
-    sock.sendall(struct.pack(">i", len(sent)) + sent)
-    print(f"Speech[length={len(sent):d}] Sent.")
-    # Receive data from the server and shut down
-    received = sock.recv(1024)
-    print(f"Recognition Results: {received}")
-    sock.close()
-    data_list = []
-  enable_trigger_record = True
-  return in_data, pyaudio.paContinue
+    """Audio recorder's stream callback function."""
+    global data_list, is_recording, enable_trigger_record
+    if is_recording:
+        data_list.append(in_data)
+        enable_trigger_record = False
+    elif len(data_list) > 0:
+        # Connect to server and send data
+        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        sock.connect((args.host_ip, args.host_port))
+        sent = "".join(data_list)
+        sock.sendall(struct.pack(">i", len(sent)) + sent)
+        print(f"Speech[length={len(sent):d}] Sent.")
+        # Receive data from the server and shut down
+        received = sock.recv(1024)
+        print(f"Recognition Results: {received}")
+        sock.close()
+        data_list = []
+    enable_trigger_record = True
+    return in_data, pyaudio.paContinue
 
 
 def main():
-  """description"""
-  p = pyaudio.PyAudio()
-  stream = p.open(
-      format = pyaudio.paInt32,
-      channels = 1,
-      rate = 16000,
-      input = True,
-      stream_callback = callback,
-      )
-  stream.start_stream()
-
-  # prepare keyboard listener
-  while 1:
-    keyboard.hook(on_press_release)
-    if keyboard.record("esc"):
-      break
-
-  # close up
-  stream.stop_stream()
-  stream.close()
-  p.terminate()
+    """description"""
+    p = pyaudio.PyAudio()
+    stream = p.open(
+        format=pyaudio.paInt32,
+        channels=1,
+        rate=16000,
+        input=True,
+        stream_callback=callback,
+    )
+    stream.start_stream()
+
+    # prepare keyboard listener
+    while 1:
+        keyboard.hook(on_press_release)
+        if keyboard.record("esc"):
+            break
+
+    # close up
+    stream.stop_stream()
+    stream.close()
+    p.terminate()
 
 
 if __name__ == "__main__":
-  main()
+    main()
diff --git a/samples/unsorted/vad_stests/stream_librosa_mfcc.py b/samples/unsorted/vad_stests/stream_librosa_mfcc.py
index c59fd07..d725313 100644
--- a/samples/unsorted/vad_stests/stream_librosa_mfcc.py
+++ b/samples/unsorted/vad_stests/stream_librosa_mfcc.py
@@ -15,89 +15,89 @@
 
 
 def a():
-  """description"""
-  sr = 44100
-  p = pyaudio.PyAudio()
-  stream = p.open(format = pyaudio.paInt16, channels = 1, rate = sr, input = True)
-  # with s as stream:
-  # librosa.get_samplerate(stream)
-  for y_block in librosa.stream(
-      stream, block_length = 256, frame_length = 2048, hop_length = 2048
-      ):
-    m_block = librosa.feature.melspectrogram(
-        y_block, sr = sr, n_fft = 2048, hop_length = 2048, center = False
+    """description"""
+    sr = 44100
+    p = pyaudio.PyAudio()
+    stream = p.open(format=pyaudio.paInt16, channels=1, rate=sr, input=True)
+    # with s as stream:
+    # librosa.get_samplerate(stream)
+    for y_block in librosa.stream(
+        stream, block_length=256, frame_length=2048, hop_length=2048
+    ):
+        m_block = librosa.feature.melspectrogram(
+            y_block, sr=sr, n_fft=2048, hop_length=2048, center=False
         )
-    print(type(m_block))
+        print(type(m_block))
 
-  stream.close()
+    stream.close()
 
 
 def b():
-  """
-
-  :return:
-  :rtype:
-  """
-
-  class AudioHandler(object):
-    """description"""
-
-    def __init__(self):
-      self.FORMAT = pyaudio.paFloat32
-      self.CHANNELS = 1
-      self.RATE = 44100
-      self.CHUNK = 1024 * 2
-      self.p = None
-      self.stream = None
-
-    def start(self):
-      """description"""
-      self.p = pyaudio.PyAudio()
-      self.stream = self.p.open(
-          format = self.FORMAT,
-          channels = self.CHANNELS,
-          rate = self.RATE,
-          input = True,
-          output = False,
-          stream_callback = self.callback,
-          frames_per_buffer = self.CHUNK,
-          )
-
-    def stop(self):
-      """description"""
-      self.stream.close()
-      self.p.terminate()
-
-    def callback(self, in_data, frame_count, time_info, flag):
-      """
-
-      :param in_data:
-      :type in_data:
-      :param frame_count:
-      :type frame_count:
-      :param time_info:
-      :type time_info:
-      :param flag:
-      :type flag:
-      :return:
-      :rtype:
-      """
-      numpy_array = numpy.frombuffer(in_data, dtype = numpy.float32)
-      a = librosa.feature.mfcc(numpy_array)
-      print(a)
-      return None, pyaudio.paContinue
-
-    def mainloop(self):
-      """description"""
-      while (
-          self.stream.is_active()
-      ):  # if using button you can set self.stream to 0 (self.stream = 0), otherwise you can use a stop condition
-        time.sleep(2.0)
-
-  audio = AudioHandler()
-  audio.start()  # open the the stream
-  audio.mainloop()  # main operations with librosa
-  audio.stop()
+    """
+
+    :return:
+    :rtype:
+    """
+
+    class AudioHandler(object):
+        """description"""
+
+        def __init__(self):
+            self.FORMAT = pyaudio.paFloat32
+            self.CHANNELS = 1
+            self.RATE = 44100
+            self.CHUNK = 1024 * 2
+            self.p = None
+            self.stream = None
+
+        def start(self):
+            """description"""
+            self.p = pyaudio.PyAudio()
+            self.stream = self.p.open(
+                format=self.FORMAT,
+                channels=self.CHANNELS,
+                rate=self.RATE,
+                input=True,
+                output=False,
+                stream_callback=self.callback,
+                frames_per_buffer=self.CHUNK,
+            )
+
+        def stop(self):
+            """description"""
+            self.stream.close()
+            self.p.terminate()
+
+        def callback(self, in_data, frame_count, time_info, flag):
+            """
+
+            :param in_data:
+            :type in_data:
+            :param frame_count:
+            :type frame_count:
+            :param time_info:
+            :type time_info:
+            :param flag:
+            :type flag:
+            :return:
+            :rtype:
+            """
+            numpy_array = numpy.frombuffer(in_data, dtype=numpy.float32)
+            a = librosa.feature.mfcc(numpy_array)
+            print(a)
+            return None, pyaudio.paContinue
+
+        def mainloop(self):
+            """description"""
+            while (
+                self.stream.is_active()
+            ):  # if using button you can set self.stream to 0 (self.stream = 0), otherwise you can use a stop condition
+                time.sleep(2.0)
+
+    audio = AudioHandler()
+    audio.start()  # open the the stream
+    audio.mainloop()  # main operations with librosa
+    audio.stop()
 
 
 b()
diff --git a/samples/unsorted/vad_stests/vad_real_time_inference.py b/samples/unsorted/vad_stests/vad_real_time_inference.py
index 21a2c90..11647d8 100644
--- a/samples/unsorted/vad_stests/vad_real_time_inference.py
+++ b/samples/unsorted/vad_stests/vad_real_time_inference.py
@@ -17,16 +17,16 @@
 import keyboard
 import pyaudio
 
-parser = argparse.ArgumentParser(description = __doc__)
+parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument(
     "--host_ip",
-    default = "localhost",
-    type = str,
-    help = "Server IP address. (default: %(default)s)",
-    )
+    default="localhost",
+    type=str,
+    help="Server IP address. (default: %(default)s)",
+)
 parser.add_argument(
-    "--host_port", default = 8086, type = int, help = "Server Port. (default: %(default)s)"
-    )
+    "--host_port", default=8086, type=int, help="Server Port. (default: %(default)s)"
+)
 args = parser.parse_args()
 
 is_recording = False
@@ -34,69 +34,69 @@
 
 
 def on_press_release(x):
-  """Keyboard callback function."""
-  global is_recording, enable_trigger_record
-  press = keyboard.KeyboardEvent("down", 28, "space")
-  release = keyboard.KeyboardEvent("up", 28, "space")
-  if x.event_type == "down" and x.name == press.name:
-    if (not is_recording) and enable_trigger_record:
-      sys.stdout.write("Start Recording ... ")
-      sys.stdout.flush()
-      is_recording = True
-  if x.event_type == "up" and x.name == release.name:
-    if is_recording == True:
-      is_recording = False
+    """Keyboard callback function."""
+    global is_recording, enable_trigger_record
+    press = keyboard.KeyboardEvent("down", 28, "space")
+    release = keyboard.KeyboardEvent("up", 28, "space")
+    if x.event_type == "down" and x.name == press.name:
+        if (not is_recording) and enable_trigger_record:
+            sys.stdout.write("Start Recording ... ")
+            sys.stdout.flush()
+            is_recording = True
+    if x.event_type == "up" and x.name == release.name:
+        if is_recording == True:
+            is_recording = False
 
 
 data_list = []
 
 
 def callback(in_data, frame_count, time_info, status):
-  """Audio recorder's stream callback function."""
-  global data_list, is_recording, enable_trigger_record
-  if is_recording:
-    data_list.append(in_data)
-    enable_trigger_record = False
-  elif len(data_list) > 0:
-    # Connect to server and send data
-    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-    sock.connect((args.host_ip, args.host_port))
-    sent = "".join(data_list)
-    sock.sendall(struct.pack(">i", len(sent)) + sent)
-    print(f"Speech[length={len(sent):d}] Sent.")
-    # Receive data from the server and shut down
-    received = sock.recv(1024)
-    print(f"Recognition Results: {received}")
-    sock.close()
-    data_list = []
-  enable_trigger_record = True
-  return in_data, pyaudio.paContinue
+    """Audio recorder's stream callback function."""
+    global data_list, is_recording, enable_trigger_record
+    if is_recording:
+        data_list.append(in_data)
+        enable_trigger_record = False
+    elif len(data_list) > 0:
+        # Connect to server and send data
+        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        sock.connect((args.host_ip, args.host_port))
+        sent = "".join(data_list)
+        sock.sendall(struct.pack(">i", len(sent)) + sent)
+        print(f"Speech[length={len(sent):d}] Sent.")
+        # Receive data from the server and shut down
+        received = sock.recv(1024)
+        print(f"Recognition Results: {received}")
+        sock.close()
+        data_list = []
+    enable_trigger_record = True
+    return in_data, pyaudio.paContinue
 
 
 def main():
-  """description"""
-  # prepare audio recorder
-  p = pyaudio.PyAudio()
-  stream = p.open(
-      format = pyaudio.paInt32,
-      channels = 1,
-      rate = 16000,
-      input = True,
-      stream_callback = callback,
-      )
-  stream.start_stream()
-
-  # prepare keyboard listener
-  while 1:
-    keyboard.hook(on_press_release)
-    if keyboard.record("esc"):
-      break
-
-  # close up
-  stream.stop_stream()
-  stream.close()
-  p.terminate()
+    """description"""
+    # prepare audio recorder
+    p = pyaudio.PyAudio()
+    stream = p.open(
+        format=pyaudio.paInt32,
+        channels=1,
+        rate=16000,
+        input=True,
+        stream_callback=callback,
+    )
+    stream.start_stream()
+
+    # prepare keyboard listener
+    while 1:
+        keyboard.hook(on_press_release)
+        if keyboard.record("esc"):
+            break
+
+    # close up
+    stream.stop_stream()
+    stream.close()
+    p.terminate()
 
 
 if __name__ == "__main__":
-  main()
+    main()
diff --git a/samples/unsorted/wha.py b/samples/unsorted/wha.py
index 5c1a58d..3c8b2ba 100644
--- a/samples/unsorted/wha.py
+++ b/samples/unsorted/wha.py
@@ -15,115 +15,115 @@
 
 
 class SWHear(object):
-  """
-  The SWHear class is made to provide access to continuously recorded
-  (and mathematically processed) microphone data.
-  """
-
-  def __init__(self, device = None, start_streaming = True):
-    """fire up the SWHear class."""
-    print(" -- initializing SWHear")
-
-    self.chunk = 4096  # number of data points to read at a time
-    self.rate = 44100  # time resolution of the recording device (Hz)
-
-    # for tape recording (continuous "tape" of recent audio)
-    self.tapeLength = 2  # seconds
-    self.tape = numpy.empty(self.rate * self.tapeLength) * numpy.nan
-
-    self.p = pyaudio.PyAudio()  # start the PyAudio class
-    if start_streaming:
-      self.stream_start()
-
-  ### LOWEST LEVEL AUDIO ACCESS
-  # pure access to microphone and stream operations
-  # keep math, plotting, FFT, etc out of here.
-
-  def stream_read(self):
-    """return values for a single chunk"""
-    data = numpy.fromstring(self.stream.read(self.chunk), dtype = numpy.int16)
-    # print(data)
-    return data
-
-  def stream_start(self):
-    """connect to the audio device and start a stream"""
-    print(" -- stream started")
-    self.stream = self.p.open(
-        format = pyaudio.paInt16,
-        channels = 1,
-        rate = self.rate,
-        input = True,
-        frames_per_buffer = self.chunk,
-        )
+    """
+    The SWHear class is made to provide access to continuously recorded
+    (and mathematically processed) microphone data.
+    """
 
-  def stream_stop(self):
-    """close the stream but keep the PyAudio instance alive."""
-    if "stream" in locals():
-      self.stream.stop_stream()
-      self.stream.close()
-    print(" -- stream CLOSED")
-
-  def close(self):
-    """gently detach from things."""
-    self.stream_stop()
-    self.p.terminate()
-
-  ### TAPE METHODS
-  # tape is like a circular magnetic ribbon of tape that's continously
-  # recorded and recorded over in a loop. self.tape contains this data.
-  # the newest data is always at the end. Don't modify data on the type,
-  # but rather do math on it (like FFT) as you read from it.
-
-  def tape_add(self):
-    """add a single chunk to the tape."""
-    self.tape[: -self.chunk] = self.tape[self.chunk:]
-    self.tape[-self.chunk:] = self.stream_read()
-
-  def tape_flush(self):
-    """completely fill tape with new data."""
-    readsInTape = int(self.rate * self.tapeLength / self.chunk)
-    print(
-        " -- flushing %d s tape with %dx%.2f ms reads"
-        % (self.tapeLength, readsInTape, self.chunk / self.rate)
+    def __init__(self, device=None, start_streaming=True):
+        """fire up the SWHear class."""
+        print(" -- initializing SWHear")
+
+        self.chunk = 4096  # number of data points to read at a time
+        self.rate = 44100  # time resolution of the recording device (Hz)
+
+        # for tape recording (continuous "tape" of recent audio)
+        self.tapeLength = 2  # seconds
+        self.tape = numpy.empty(self.rate * self.tapeLength) * numpy.nan
+
+        self.p = pyaudio.PyAudio()  # start the PyAudio class
+        if start_streaming:
+            self.stream_start()
+
+    ### LOWEST LEVEL AUDIO ACCESS
+    # pure access to microphone and stream operations
+    # keep math, plotting, FFT, etc out of here.
+
+    def stream_read(self):
+        """return values for a single chunk"""
+        data = numpy.fromstring(self.stream.read(self.chunk), dtype=numpy.int16)
+        # print(data)
+        return data
+
+    def stream_start(self):
+        """connect to the audio device and start a stream"""
+        print(" -- stream started")
+        self.stream = self.p.open(
+            format=pyaudio.paInt16,
+            channels=1,
+            rate=self.rate,
+            input=True,
+            frames_per_buffer=self.chunk,
         )
-    for i in range(readsInTape):
-      self.tape_add()
 
-  def tape_forever(self, plot_sec = 0.25):
-    """
-
-    :param plot_sec:
-    :type plot_sec:
-    :return:
-    :rtype:
-    """
-    t1 = 0
-    try:
-      while True:
-        self.tape_add()
-        if (time.time() - t1) > plot_sec:
-          t1 = time.time()
-          self.tape_plot()
-    except:
-      print(" ~~ exception (keyboard?)")
-      return
-
-  def tape_plot(self, saveAs = "03.png"):
-    """plot what's in the tape."""
-    pylab.plot(numpy.arange(len(self.tape)) / self.rate, self.tape)
-    pylab.axis([0, self.tapeLength, -(2 ** 16) / 2, 2 ** 16 / 2])
-    if saveAs:
-      t1 = time.time()
-      pylab.savefig(saveAs, dpi = 50)
-      print(f"plotting saving took {(time.time() - t1) * 1000:.02f} ms")
-    else:
-      pylab.show()
-      print()  # good for IPython
-    pylab.close("all")
+    def stream_stop(self):
+        """close the stream but keep the PyAudio instance alive."""
+        if "stream" in locals():
+            self.stream.stop_stream()
+            self.stream.close()
+        print(" -- stream CLOSED")
+
+    def close(self):
+        """gently detach from things."""
+        self.stream_stop()
+        self.p.terminate()
+
+    ### TAPE METHODS
+    # tape is like a circular magnetic ribbon of tape that's continously
+    # recorded and recorded over in a loop. self.tape contains this data.
+    # the newest data is always at the end. Don't modify data on the type,
+    # but rather do math on it (like FFT) as you read from it.
+
+    def tape_add(self):
+        """add a single chunk to the tape."""
+        self.tape[: -self.chunk] = self.tape[self.chunk :]
+        self.tape[-self.chunk :] = self.stream_read()
+
+    def tape_flush(self):
+        """completely fill tape with new data."""
+        readsInTape = int(self.rate * self.tapeLength / self.chunk)
+        print(
+            " -- flushing %d s tape with %dx%.2f ms reads"
+            % (self.tapeLength, readsInTape, self.chunk / self.rate)
+        )
+        for i in range(readsInTape):
+            self.tape_add()
+
+    def tape_forever(self, plot_sec=0.25):
+        """
+
+        :param plot_sec:
+        :type plot_sec:
+        :return:
+        :rtype:
+        """
+        t1 = 0
+        try:
+            while True:
+                self.tape_add()
+                if (time.time() - t1) > plot_sec:
+                    t1 = time.time()
+                    self.tape_plot()
+        except:
+            print(" ~~ exception (keyboard?)")
+            return
+
+    def tape_plot(self, saveAs="03.png"):
+        """plot what's in the tape."""
+        pylab.plot(numpy.arange(len(self.tape)) / self.rate, self.tape)
+        pylab.axis([0, self.tapeLength, -(2**16) / 2, 2**16 / 2])
+        if saveAs:
+            t1 = time.time()
+            pylab.savefig(saveAs, dpi=50)
+            print(f"plotting saving took {(time.time() - t1) * 1000:.02f} ms")
+        else:
+            pylab.show()
+            print()  # good for IPython
+        pylab.close("all")
 
 
 if __name__ == "__main__":
-  ear = SWHear()
-  ear.tape_forever()
-  ear.close()
-  print("DONE")
+    ear = SWHear()
+    ear.tape_forever()
+    ear.close()
+    print("DONE")
diff --git a/tests/data/categories.py b/tests/data/categories.py
index 7997a73..99e40e1 100644
--- a/tests/data/categories.py
+++ b/tests/data/categories.py
@@ -13,36 +13,36 @@
 
 
 def labels_index_test():
-  """description"""
-  train_set = SpeechCommands()
+    """description"""
+    train_set = SpeechCommands()
 
-  word_start = "yes"
-  index = train_set.label_to_index(word_start)
-  word_recovered = train_set.index_to_label(index)
+    word_start = "yes"
+    index = train_set.label_to_index(word_start)
+    word_recovered = train_set.index_to_label(index)
 
-  print(word_start, "-->", index, "-->", word_recovered)
+    print(word_start, "-->", index, "-->", word_recovered)
 
 
 def labels_index_same_test():
-  """description"""
-  train_set = SpeechCommands()
-  valid_set = SpeechCommands(split = SplitEnum.validation)
-  test_set = SpeechCommands(split = SplitEnum.testing)
+    """description"""
+    train_set = SpeechCommands()
+    valid_set = SpeechCommands(split=SplitEnum.validation)
+    test_set = SpeechCommands(split=SplitEnum.testing)
 
-  word_start = "yes"
-  index_train = train_set.label_to_index(word_start)
-  word_recovered_train = train_set.index_to_label(index_train)
+    word_start = "yes"
+    index_train = train_set.label_to_index(word_start)
+    word_recovered_train = train_set.index_to_label(index_train)
 
-  index_valid = valid_set.label_to_index(word_start)
-  word_recovered_valid = valid_set.index_to_label(index_valid)
+    index_valid = valid_set.label_to_index(word_start)
+    word_recovered_valid = valid_set.index_to_label(index_valid)
 
-  index_test = test_set.label_to_index(word_start)
-  word_recovered_test = test_set.index_to_label(index_test)
+    index_test = test_set.label_to_index(word_start)
+    word_recovered_test = test_set.index_to_label(index_test)
 
-  assert index_test == index_train == index_valid
-  assert word_recovered_train == word_recovered_valid == word_recovered_test
+    assert index_test == index_train == index_valid
+    assert word_recovered_train == word_recovered_valid == word_recovered_test
 
 
 if __name__ == "__main__":
-  labels_index_test()
-  labels_index_same_test()
+    labels_index_test()
+    labels_index_same_test()
diff --git a/tests/regression/reconstruction/torch_audio_demo.py b/tests/regression/reconstruction/torch_audio_demo.py
index b255e01..f000a73 100644
--- a/tests/regression/reconstruction/torch_audio_demo.py
+++ b/tests/regression/reconstruction/torch_audio_demo.py
@@ -22,827 +22,827 @@
 
 
 def _test_torchscript_functional_shape(py_method, *args, **kwargs):
-  jit_method = torch.jit.script(py_method)
+    jit_method = torch.jit.script(py_method)
 
-  jit_out = jit_method(*args, **kwargs)
-  py_out = py_method(*args, **kwargs)
+    jit_out = jit_method(*args, **kwargs)
+    py_out = py_method(*args, **kwargs)
 
-  assert jit_out.shape == py_out.shape
-  return jit_out, py_out
+    assert jit_out.shape == py_out.shape
+    return jit_out, py_out
 
 
 def _test_torchscript_functional(py_method, *args, **kwargs):
-  jit_out, py_out = _test_torchscript_functional_shape(py_method, *args, **kwargs)
-  assert torch.allclose(jit_out, py_out)
+    jit_out, py_out = _test_torchscript_functional_shape(py_method, *args, **kwargs)
+    assert torch.allclose(jit_out, py_out)
 
 
 class TestFunctional(unittest.TestCase):
-  data_sizes = [(2, 20), (3, 15), (4, 10)]
-  number_of_trials = 100
-  specgram = torch.tensor([1.0, 2.0, 3.0, 4.0])
-
-  test_dirpath, test_dir = common_utils.create_temp_assets_dir()
-
-  test_filepath = os.path.join(
-      test_dirpath, "assets", "steam-train-whistle-daniel_simon.mp3"
-      )
-  waveform_train, sr_train = torchaudio.load(test_filepath)
-
-  def test_torchscript_spectrogram(self):
-
-    tensor = torch.rand((1, 1000))
-    n_fft = 400
-    ws = 400
-    hop = 200
-    pad = 0
-    window = torch.hann_window(ws)
-    power = 2
-    normalize = False
-
-    _test_torchscript_functional(
-        F.spectrogram, tensor, pad, window, n_fft, hop, ws, power, normalize
-        )
-
-  def test_torchscript_griffinlim(self):
-    tensor = torch.rand((1, 201, 6))
-    n_fft = 400
-    ws = 400
-    hop = 200
-    window = torch.hann_window(ws)
-    power = 2
-    normalize = False
-    momentum = 0.99
-    n_iter = 32
-    length = 1000
-    init = 0
-
-    _test_torchscript_functional(
-        F.griffinlim,
-        tensor,
-        window,
-        n_fft,
-        hop,
-        ws,
-        power,
-        normalize,
-        n_iter,
-        momentum,
-        length,
-        0,
-        )
+    data_sizes = [(2, 20), (3, 15), (4, 10)]
+    number_of_trials = 100
+    specgram = torch.tensor([1.0, 2.0, 3.0, 4.0])
 
-  def test_batch_griffinlim(self):
-
-    torch.random.manual_seed(42)
-    tensor = torch.rand((1, 201, 6))
-
-    n_fft = 400
-    ws = 400
-    hop = 200
-    window = torch.hann_window(ws)
-    power = 2
-    normalize = False
-    momentum = 0.99
-    n_iter = 32
-    length = 1000
-
-    self._test_batch(
-        F.griffinlim,
-        tensor,
-        window,
-        n_fft,
-        hop,
-        ws,
-        power,
-        normalize,
-        n_iter,
-        momentum,
-        length,
-        0,
-        atol = 5e-5,
-        )
-
-  def _test_compute_deltas(
-      self, specgram, expected, win_length = 3, atol = 1e-6, rtol = 1e-8
-      ):
-    computed = F.compute_deltas(specgram, win_length = win_length)
-    self.assertTrue(
-        computed.shape == expected.shape, (computed.shape, expected.shape)
-        )
-    torch.testing.assert_allclose(computed, expected, atol = atol, rtol = rtol)
-
-  def test_compute_deltas_onechannel(self):
-    specgram = self.specgram.unsqueeze(0).unsqueeze(0)
-    expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]])
-    self._test_compute_deltas(specgram, expected)
-
-  def test_compute_deltas_twochannel(self):
-    specgram = self.specgram.repeat(1, 2, 1)
-    expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5], [0.5, 1.0, 1.0, 0.5]]])
-    self._test_compute_deltas(specgram, expected)
-
-  def test_compute_deltas_randn(self):
-    channel = 13
-    n_mfcc = channel * 3
-    time = 1021
-    win_length = 2 * 7 + 1
-    specgram = torch.randn(channel, n_mfcc, time)
-    computed = F.compute_deltas(specgram, win_length = win_length)
-
-    self.assertTrue(
-        computed.shape == specgram.shape, (computed.shape, specgram.shape)
-        )
+    test_dirpath, test_dir = common_utils.create_temp_assets_dir()
 
-    _test_torchscript_functional(F.compute_deltas, specgram, win_length = win_length)
+    test_filepath = os.path.join(
+        test_dirpath, "assets", "steam-train-whistle-daniel_simon.mp3"
+    )
+    waveform_train, sr_train = torchaudio.load(test_filepath)
 
-  def test_batch_pitch(self):
-    waveform, sample_rate = torchaudio.load(self.test_filepath)
-    self._test_batch(F.detect_pitch_frequency, waveform, sample_rate)
+    def test_torchscript_spectrogram(self):
 
-  def test_jit_pitch(self):
-    waveform, sample_rate = torchaudio.load(self.test_filepath)
-    _test_torchscript_functional(F.detect_pitch_frequency, waveform, sample_rate)
+        tensor = torch.rand((1, 1000))
+        n_fft = 400
+        ws = 400
+        hop = 200
+        pad = 0
+        window = torch.hann_window(ws)
+        power = 2
+        normalize = False
 
-  def _compare_estimate(self, sound, estimate, atol = 1e-6, rtol = 1e-8):
-    # trim sound for case when constructed signal is shorter than original
-    sound = sound[..., : estimate.size(-1)]
+        _test_torchscript_functional(
+            F.spectrogram, tensor, pad, window, n_fft, hop, ws, power, normalize
+        )
 
-    self.assertTrue(sound.shape == estimate.shape, (sound.shape, estimate.shape))
-    self.assertTrue(torch.allclose(sound, estimate, atol = atol, rtol = rtol))
+    def test_torchscript_griffinlim(self):
+        tensor = torch.rand((1, 201, 6))
+        n_fft = 400
+        ws = 400
+        hop = 200
+        window = torch.hann_window(ws)
+        power = 2
+        normalize = False
+        momentum = 0.99
+        n_iter = 32
+        length = 1000
+        init = 0
+
+        _test_torchscript_functional(
+            F.griffinlim,
+            tensor,
+            window,
+            n_fft,
+            hop,
+            ws,
+            power,
+            normalize,
+            n_iter,
+            momentum,
+            length,
+            0,
+        )
 
-  def _test_istft_is_inverse_of_stft(self, kwargs):
-    # generates a random sound signal for each tril and then does the stft/istft
-    # operation to check whether we can reconstruct signal
-    for data_size in self.data_sizes:
-      for i in range(self.number_of_trials):
-        sound = common_utils.random_float_tensor(i, data_size)
+    def test_batch_griffinlim(self):
+
+        torch.random.manual_seed(42)
+        tensor = torch.rand((1, 201, 6))
+
+        n_fft = 400
+        ws = 400
+        hop = 200
+        window = torch.hann_window(ws)
+        power = 2
+        normalize = False
+        momentum = 0.99
+        n_iter = 32
+        length = 1000
+
+        self._test_batch(
+            F.griffinlim,
+            tensor,
+            window,
+            n_fft,
+            hop,
+            ws,
+            power,
+            normalize,
+            n_iter,
+            momentum,
+            length,
+            0,
+            atol=5e-5,
+        )
 
-        stft = torch.stft(sound, **kwargs)
-        estimate = torchaudio.functional.istft(
-            stft, length = sound.size(1), **kwargs
-            )
+    def _test_compute_deltas(
+        self, specgram, expected, win_length=3, atol=1e-6, rtol=1e-8
+    ):
+        computed = F.compute_deltas(specgram, win_length=win_length)
+        self.assertTrue(
+            computed.shape == expected.shape, (computed.shape, expected.shape)
+        )
+        torch.testing.assert_allclose(computed, expected, atol=atol, rtol=rtol)
+
+    def test_compute_deltas_onechannel(self):
+        specgram = self.specgram.unsqueeze(0).unsqueeze(0)
+        expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]])
+        self._test_compute_deltas(specgram, expected)
+
+    def test_compute_deltas_twochannel(self):
+        specgram = self.specgram.repeat(1, 2, 1)
+        expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5], [0.5, 1.0, 1.0, 0.5]]])
+        self._test_compute_deltas(specgram, expected)
+
+    def test_compute_deltas_randn(self):
+        channel = 13
+        n_mfcc = channel * 3
+        time = 1021
+        win_length = 2 * 7 + 1
+        specgram = torch.randn(channel, n_mfcc, time)
+        computed = F.compute_deltas(specgram, win_length=win_length)
+
+        self.assertTrue(
+            computed.shape == specgram.shape, (computed.shape, specgram.shape)
+        )
 
-        self._compare_estimate(sound, estimate)
-
-  def test_istft_is_inverse_of_stft1(self):
-    # hann_window, centered, normalized, onesided
-    kwargs1 = {
-        "n_fft":      12,
-        "hop_length": 4,
-        "win_length": 12,
-        "window":     torch.hann_window(12),
-        "center":     True,
-        "pad_mode":   "reflect",
-        "normalized": True,
-        "onesided":   True,
+        _test_torchscript_functional(F.compute_deltas, specgram, win_length=win_length)
+
+    def test_batch_pitch(self):
+        waveform, sample_rate = torchaudio.load(self.test_filepath)
+        self._test_batch(F.detect_pitch_frequency, waveform, sample_rate)
+
+    def test_jit_pitch(self):
+        waveform, sample_rate = torchaudio.load(self.test_filepath)
+        _test_torchscript_functional(F.detect_pitch_frequency, waveform, sample_rate)
+
+    def _compare_estimate(self, sound, estimate, atol=1e-6, rtol=1e-8):
+        # trim sound for case when constructed signal is shorter than original
+        sound = sound[..., : estimate.size(-1)]
+
+        self.assertTrue(sound.shape == estimate.shape, (sound.shape, estimate.shape))
+        self.assertTrue(torch.allclose(sound, estimate, atol=atol, rtol=rtol))
+
+    def _test_istft_is_inverse_of_stft(self, kwargs):
+        # generates a random sound signal for each tril and then does the stft/istft
+        # operation to check whether we can reconstruct signal
+        for data_size in self.data_sizes:
+            for i in range(self.number_of_trials):
+                sound = common_utils.random_float_tensor(i, data_size)
+
+                stft = torch.stft(sound, **kwargs)
+                estimate = torchaudio.functional.istft(
+                    stft, length=sound.size(1), **kwargs
+                )
+
+                self._compare_estimate(sound, estimate)
+
+    def test_istft_is_inverse_of_stft1(self):
+        # hann_window, centered, normalized, onesided
+        kwargs1 = {
+            "n_fft": 12,
+            "hop_length": 4,
+            "win_length": 12,
+            "window": torch.hann_window(12),
+            "center": True,
+            "pad_mode": "reflect",
+            "normalized": True,
+            "onesided": True,
         }
 
-    self._test_istft_is_inverse_of_stft(kwargs1)
-
-  def test_istft_is_inverse_of_stft2(self):
-    # hann_window, centered, not normalized, not onesided
-    kwargs2 = {
-        "n_fft":      12,
-        "hop_length": 2,
-        "win_length": 8,
-        "window":     torch.hann_window(8),
-        "center":     True,
-        "pad_mode":   "reflect",
-        "normalized": False,
-        "onesided":   False,
+        self._test_istft_is_inverse_of_stft(kwargs1)
+
+    def test_istft_is_inverse_of_stft2(self):
+        # hann_window, centered, not normalized, not onesided
+        kwargs2 = {
+            "n_fft": 12,
+            "hop_length": 2,
+            "win_length": 8,
+            "window": torch.hann_window(8),
+            "center": True,
+            "pad_mode": "reflect",
+            "normalized": False,
+            "onesided": False,
         }
 
-    self._test_istft_is_inverse_of_stft(kwargs2)
-
-  def test_istft_is_inverse_of_stft3(self):
-    # hamming_window, centered, normalized, not onesided
-    kwargs3 = {
-        "n_fft":      15,
-        "hop_length": 3,
-        "win_length": 11,
-        "window":     torch.hamming_window(11),
-        "center":     True,
-        "pad_mode":   "constant",
-        "normalized": True,
-        "onesided":   False,
+        self._test_istft_is_inverse_of_stft(kwargs2)
+
+    def test_istft_is_inverse_of_stft3(self):
+        # hamming_window, centered, normalized, not onesided
+        kwargs3 = {
+            "n_fft": 15,
+            "hop_length": 3,
+            "win_length": 11,
+            "window": torch.hamming_window(11),
+            "center": True,
+            "pad_mode": "constant",
+            "normalized": True,
+            "onesided": False,
         }
 
-    self._test_istft_is_inverse_of_stft(kwargs3)
-
-  def test_istft_is_inverse_of_stft4(self):
-    # hamming_window, not centered, not normalized, onesided
-    # window same size as n_fft
-    kwargs4 = {
-        "n_fft":      5,
-        "hop_length": 2,
-        "win_length": 5,
-        "window":     torch.hamming_window(5),
-        "center":     False,
-        "pad_mode":   "constant",
-        "normalized": False,
-        "onesided":   True,
+        self._test_istft_is_inverse_of_stft(kwargs3)
+
+    def test_istft_is_inverse_of_stft4(self):
+        # hamming_window, not centered, not normalized, onesided
+        # window same size as n_fft
+        kwargs4 = {
+            "n_fft": 5,
+            "hop_length": 2,
+            "win_length": 5,
+            "window": torch.hamming_window(5),
+            "center": False,
+            "pad_mode": "constant",
+            "normalized": False,
+            "onesided": True,
         }
 
-    self._test_istft_is_inverse_of_stft(kwargs4)
-
-  def test_istft_is_inverse_of_stft5(self):
-    # hamming_window, not centered, not normalized, not onesided
-    # window same size as n_fft
-    kwargs5 = {
-        "n_fft":      3,
-        "hop_length": 2,
-        "win_length": 3,
-        "window":     torch.hamming_window(3),
-        "center":     False,
-        "pad_mode":   "reflect",
-        "normalized": False,
-        "onesided":   False,
+        self._test_istft_is_inverse_of_stft(kwargs4)
+
+    def test_istft_is_inverse_of_stft5(self):
+        # hamming_window, not centered, not normalized, not onesided
+        # window same size as n_fft
+        kwargs5 = {
+            "n_fft": 3,
+            "hop_length": 2,
+            "win_length": 3,
+            "window": torch.hamming_window(3),
+            "center": False,
+            "pad_mode": "reflect",
+            "normalized": False,
+            "onesided": False,
         }
 
-    self._test_istft_is_inverse_of_stft(kwargs5)
+        self._test_istft_is_inverse_of_stft(kwargs5)
 
-  def test_istft_of_ones(self):
-    # stft = torch.stft(torch.ones(4), 4)
-    stft = torch.tensor(
-        [
-            [[4.0, 0.0], [4.0, 0.0], [4.0, 0.0], [4.0, 0.0], [4.0, 0.0]],
-            [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]],
-            [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]],
+    def test_istft_of_ones(self):
+        # stft = torch.stft(torch.ones(4), 4)
+        stft = torch.tensor(
+            [
+                [[4.0, 0.0], [4.0, 0.0], [4.0, 0.0], [4.0, 0.0], [4.0, 0.0]],
+                [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]],
+                [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]],
             ]
         )
 
-    estimate = torchaudio.functional.istft(stft, n_fft = 4, length = 4)
-    self._compare_estimate(torch.ones(4), estimate)
-
-  def test_istft_of_zeros(self):
-    # stft = torch.stft(torch.zeros(4), 4)
-    stft = torch.zeros((3, 5, 2))
-
-    estimate = torchaudio.functional.istft(stft, n_fft = 4, length = 4)
-    self._compare_estimate(torch.zeros(4), estimate)
-
-  def test_istft_requires_overlap_windows(self):
-    # the window is size 1 but it hops 20 so there is a gap which throw an error
-    stft = torch.zeros((3, 5, 2))
-    self.assertRaises(
-        AssertionError,
-        torchaudio.functional.istft,
-        stft,
-        n_fft = 4,
-        hop_length = 20,
-        win_length = 1,
-        window = torch.ones(1),
+        estimate = torchaudio.functional.istft(stft, n_fft=4, length=4)
+        self._compare_estimate(torch.ones(4), estimate)
+
+    def test_istft_of_zeros(self):
+        # stft = torch.stft(torch.zeros(4), 4)
+        stft = torch.zeros((3, 5, 2))
+
+        estimate = torchaudio.functional.istft(stft, n_fft=4, length=4)
+        self._compare_estimate(torch.zeros(4), estimate)
+
+    def test_istft_requires_overlap_windows(self):
+        # the window is size 1 but it hops 20 so there is a gap which throw an error
+        stft = torch.zeros((3, 5, 2))
+        self.assertRaises(
+            AssertionError,
+            torchaudio.functional.istft,
+            stft,
+            n_fft=4,
+            hop_length=20,
+            win_length=1,
+            window=torch.ones(1),
         )
 
-  def test_istft_requires_nola(self):
-    stft = torch.zeros((3, 5, 2))
-    kwargs_ok = {
-        "n_fft":      4,
-        "win_length": 4,
-        "window":     torch.ones(4),
+    def test_istft_requires_nola(self):
+        stft = torch.zeros((3, 5, 2))
+        kwargs_ok = {
+            "n_fft": 4,
+            "win_length": 4,
+            "window": torch.ones(4),
         }
 
-    kwargs_not_ok = {
-        "n_fft":      4,
-        "win_length": 4,
-        "window":     torch.zeros(4),
+        kwargs_not_ok = {
+            "n_fft": 4,
+            "win_length": 4,
+            "window": torch.zeros(4),
         }
 
-    # A window of ones meets NOLA but a window of zeros does not. This should
-    # throw an error.
-    torchaudio.functional.istft(stft, **kwargs_ok)
-    self.assertRaises(
-        AssertionError, torchaudio.functional.istft, stft, **kwargs_not_ok
+        # A window of ones meets NOLA but a window of zeros does not. This should
+        # throw an error.
+        torchaudio.functional.istft(stft, **kwargs_ok)
+        self.assertRaises(
+            AssertionError, torchaudio.functional.istft, stft, **kwargs_not_ok
         )
 
-  def test_istft_requires_non_empty(self):
-    self.assertRaises(
-        AssertionError, torchaudio.functional.istft, torch.zeros((3, 0, 2)), 2
+    def test_istft_requires_non_empty(self):
+        self.assertRaises(
+            AssertionError, torchaudio.functional.istft, torch.zeros((3, 0, 2)), 2
         )
-    self.assertRaises(
-        AssertionError, torchaudio.functional.istft, torch.zeros((0, 3, 2)), 2
+        self.assertRaises(
+            AssertionError, torchaudio.functional.istft, torch.zeros((0, 3, 2)), 2
         )
 
-  def _test_istft_of_sine(self, amplitude, L, n):
-    # stft of amplitude*sin(2*pi/L*n*x) with the hop length and window size equaling L
-    x = torch.arange(2 * L + 1, dtype = torch.get_default_dtype())
-    sound = amplitude * torch.sin(2 * math.pi / L * x * n)
-    # stft = torch.stft(sound, L, hop_length=L, win_length=L,
-    #                   window=torch.ones(L), center=False, normalized=False)
-    stft = torch.zeros((L // 2 + 1, 2, 2))
-    stft_largest_val = (amplitude * L) / 2.0
-    if n < stft.size(0):
-      stft[n, :, 1] = -stft_largest_val
-
-    if 0 <= L - n < stft.size(0):
-      # symmetric about L // 2
-      stft[L - n, :, 1] = stft_largest_val
-
-    estimate = torchaudio.functional.istft(
-        stft,
-        L,
-        hop_length = L,
-        win_length = L,
-        window = torch.ones(L),
-        center = False,
-        normalized = False,
+    def _test_istft_of_sine(self, amplitude, L, n):
+        # stft of amplitude*sin(2*pi/L*n*x) with the hop length and window size equaling L
+        x = torch.arange(2 * L + 1, dtype=torch.get_default_dtype())
+        sound = amplitude * torch.sin(2 * math.pi / L * x * n)
+        # stft = torch.stft(sound, L, hop_length=L, win_length=L,
+        #                   window=torch.ones(L), center=False, normalized=False)
+        stft = torch.zeros((L // 2 + 1, 2, 2))
+        stft_largest_val = (amplitude * L) / 2.0
+        if n < stft.size(0):
+            stft[n, :, 1] = -stft_largest_val
+
+        if 0 <= L - n < stft.size(0):
+            # symmetric about L // 2
+            stft[L - n, :, 1] = stft_largest_val
+
+        estimate = torchaudio.functional.istft(
+            stft,
+            L,
+            hop_length=L,
+            win_length=L,
+            window=torch.ones(L),
+            center=False,
+            normalized=False,
         )
-    # There is a larger error due to the scaling of amplitude
-    self._compare_estimate(sound, estimate, atol = 1e-3)
-
-  def test_istft_of_sine(self):
-    self._test_istft_of_sine(amplitude = 123, L = 5, n = 1)
-    self._test_istft_of_sine(amplitude = 150, L = 5, n = 2)
-    self._test_istft_of_sine(amplitude = 111, L = 5, n = 3)
-    self._test_istft_of_sine(amplitude = 160, L = 7, n = 4)
-    self._test_istft_of_sine(amplitude = 145, L = 8, n = 5)
-    self._test_istft_of_sine(amplitude = 80, L = 9, n = 6)
-    self._test_istft_of_sine(amplitude = 99, L = 10, n = 7)
-
-  def _test_linearity_of_istft(self, data_size, kwargs, atol = 1e-6, rtol = 1e-8):
-    for i in range(self.number_of_trials):
-      tensor1 = common_utils.random_float_tensor(i, data_size)
-      tensor2 = common_utils.random_float_tensor(i * 2, data_size)
-      a, b = torch.rand(2)
-      istft1 = torchaudio.functional.istft(tensor1, **kwargs)
-      istft2 = torchaudio.functional.istft(tensor2, **kwargs)
-      istft = a * istft1 + b * istft2
-      estimate = torchaudio.functional.istft(a * tensor1 + b * tensor2, **kwargs)
-      self._compare_estimate(istft, estimate, atol, rtol)
-
-  def test_linearity_of_istft1(self):
-    # hann_window, centered, normalized, onesided
-    kwargs1 = {
-        "n_fft":      12,
-        "window":     torch.hann_window(12),
-        "center":     True,
-        "pad_mode":   "reflect",
-        "normalized": True,
-        "onesided":   True,
+        # There is a larger error due to the scaling of amplitude
+        self._compare_estimate(sound, estimate, atol=1e-3)
+
+    def test_istft_of_sine(self):
+        self._test_istft_of_sine(amplitude=123, L=5, n=1)
+        self._test_istft_of_sine(amplitude=150, L=5, n=2)
+        self._test_istft_of_sine(amplitude=111, L=5, n=3)
+        self._test_istft_of_sine(amplitude=160, L=7, n=4)
+        self._test_istft_of_sine(amplitude=145, L=8, n=5)
+        self._test_istft_of_sine(amplitude=80, L=9, n=6)
+        self._test_istft_of_sine(amplitude=99, L=10, n=7)
+
+    def _test_linearity_of_istft(self, data_size, kwargs, atol=1e-6, rtol=1e-8):
+        for i in range(self.number_of_trials):
+            tensor1 = common_utils.random_float_tensor(i, data_size)
+            tensor2 = common_utils.random_float_tensor(i * 2, data_size)
+            a, b = torch.rand(2)
+            istft1 = torchaudio.functional.istft(tensor1, **kwargs)
+            istft2 = torchaudio.functional.istft(tensor2, **kwargs)
+            istft = a * istft1 + b * istft2
+            estimate = torchaudio.functional.istft(a * tensor1 + b * tensor2, **kwargs)
+            self._compare_estimate(istft, estimate, atol, rtol)
+
+    def test_linearity_of_istft1(self):
+        # hann_window, centered, normalized, onesided
+        kwargs1 = {
+            "n_fft": 12,
+            "window": torch.hann_window(12),
+            "center": True,
+            "pad_mode": "reflect",
+            "normalized": True,
+            "onesided": True,
         }
-    data_size = (2, 7, 7, 2)
-    self._test_linearity_of_istft(data_size, kwargs1)
-
-  def test_linearity_of_istft2(self):
-    # hann_window, centered, not normalized, not onesided
-    kwargs2 = {
-        "n_fft":      12,
-        "window":     torch.hann_window(12),
-        "center":     True,
-        "pad_mode":   "reflect",
-        "normalized": False,
-        "onesided":   False,
+        data_size = (2, 7, 7, 2)
+        self._test_linearity_of_istft(data_size, kwargs1)
+
+    def test_linearity_of_istft2(self):
+        # hann_window, centered, not normalized, not onesided
+        kwargs2 = {
+            "n_fft": 12,
+            "window": torch.hann_window(12),
+            "center": True,
+            "pad_mode": "reflect",
+            "normalized": False,
+            "onesided": False,
         }
-    data_size = (2, 12, 7, 2)
-    self._test_linearity_of_istft(data_size, kwargs2)
-
-  def test_linearity_of_istft3(self):
-    # hamming_window, centered, normalized, not onesided
-    kwargs3 = {
-        "n_fft":      12,
-        "window":     torch.hamming_window(12),
-        "center":     True,
-        "pad_mode":   "constant",
-        "normalized": True,
-        "onesided":   False,
+        data_size = (2, 12, 7, 2)
+        self._test_linearity_of_istft(data_size, kwargs2)
+
+    def test_linearity_of_istft3(self):
+        # hamming_window, centered, normalized, not onesided
+        kwargs3 = {
+            "n_fft": 12,
+            "window": torch.hamming_window(12),
+            "center": True,
+            "pad_mode": "constant",
+            "normalized": True,
+            "onesided": False,
         }
-    data_size = (2, 12, 7, 2)
-    self._test_linearity_of_istft(data_size, kwargs3)
-
-  def test_linearity_of_istft4(self):
-    # hamming_window, not centered, not normalized, onesided
-    kwargs4 = {
-        "n_fft":      12,
-        "window":     torch.hamming_window(12),
-        "center":     False,
-        "pad_mode":   "constant",
-        "normalized": False,
-        "onesided":   True,
+        data_size = (2, 12, 7, 2)
+        self._test_linearity_of_istft(data_size, kwargs3)
+
+    def test_linearity_of_istft4(self):
+        # hamming_window, not centered, not normalized, onesided
+        kwargs4 = {
+            "n_fft": 12,
+            "window": torch.hamming_window(12),
+            "center": False,
+            "pad_mode": "constant",
+            "normalized": False,
+            "onesided": True,
         }
-    data_size = (2, 7, 3, 2)
-    self._test_linearity_of_istft(data_size, kwargs4, atol = 1e-5, rtol = 1e-8)
+        data_size = (2, 7, 3, 2)
+        self._test_linearity_of_istft(data_size, kwargs4, atol=1e-5, rtol=1e-8)
 
-  def test_batch_istft(self):
+    def test_batch_istft(self):
 
-    stft = torch.tensor(
-        [
-            [[4.0, 0.0], [4.0, 0.0], [4.0, 0.0], [4.0, 0.0], [4.0, 0.0]],
-            [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]],
-            [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]],
+        stft = torch.tensor(
+            [
+                [[4.0, 0.0], [4.0, 0.0], [4.0, 0.0], [4.0, 0.0], [4.0, 0.0]],
+                [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]],
+                [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]],
             ]
         )
 
-    self._test_batch(F.istft, stft, n_fft = 4, length = 4)
+        self._test_batch(F.istft, stft, n_fft=4, length=4)
 
-  def _test_create_fb(
-      self, n_mels = 40, sample_rate = 22050, n_fft = 2048, fmin = 0.0, fmax = 8000.0
-      ):
+    def _test_create_fb(
+        self, n_mels=40, sample_rate=22050, n_fft=2048, fmin=0.0, fmax=8000.0
+    ):
 
-    librosa_fb = librosa.filters.mel(
-        sr = sample_rate,
-        n_fft = n_fft,
-        n_mels = n_mels,
-        fmax = fmax,
-        fmin = fmin,
-        htk = True,
-        norm = None,
+        librosa_fb = librosa.filters.mel(
+            sr=sample_rate,
+            n_fft=n_fft,
+            n_mels=n_mels,
+            fmax=fmax,
+            fmin=fmin,
+            htk=True,
+            norm=None,
         )
-    fb = F.create_fb_matrix(
-        sample_rate = sample_rate,
-        n_mels = n_mels,
-        f_max = fmax,
-        f_min = fmin,
-        n_freqs = (n_fft // 2 + 1),
+        fb = F.create_fb_matrix(
+            sample_rate=sample_rate,
+            n_mels=n_mels,
+            f_max=fmax,
+            f_min=fmin,
+            n_freqs=(n_fft // 2 + 1),
         )
 
-    for i_mel_bank in range(n_mels):
-      assert torch.allclose(
-          fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), atol = 1e-4
-          )
-
-  def test_create_fb(self):
-    self._test_create_fb()
-    self._test_create_fb(n_mels = 128, sample_rate = 44100)
-    self._test_create_fb(n_mels = 128, fmin = 2000.0, fmax = 5000.0)
-    self._test_create_fb(n_mels = 56, fmin = 100.0, fmax = 9000.0)
-    self._test_create_fb(n_mels = 56, fmin = 800.0, fmax = 900.0)
-    self._test_create_fb(n_mels = 56, fmin = 1900.0, fmax = 900.0)
-    self._test_create_fb(n_mels = 10, fmin = 1900.0, fmax = 900.0)
-
-  def test_gain(self):
-    waveform_gain = F.gain(self.waveform_train, 3)
-    self.assertTrue(waveform_gain.abs().max().item(), 1.0)
-
-    E = torchaudio.sox_effects.SoxEffectsChain()
-    E.set_input_file(self.test_filepath)
-    E.append_effect_to_chain("gain", [3])
-    sox_gain_waveform = E.sox_build_flow_effects()[0]
-
-    self.assertTrue(torch.allclose(waveform_gain, sox_gain_waveform, atol = 1e-04))
-
-  def test_dither(self):
-    waveform_dithered = F.dither(self.waveform_train)
-    waveform_dithered_noiseshaped = F.dither(
-        self.waveform_train, noise_shaping = True
+        for i_mel_bank in range(n_mels):
+            assert torch.allclose(
+                fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), atol=1e-4
+            )
+
+    def test_create_fb(self):
+        self._test_create_fb()
+        self._test_create_fb(n_mels=128, sample_rate=44100)
+        self._test_create_fb(n_mels=128, fmin=2000.0, fmax=5000.0)
+        self._test_create_fb(n_mels=56, fmin=100.0, fmax=9000.0)
+        self._test_create_fb(n_mels=56, fmin=800.0, fmax=900.0)
+        self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0)
+        self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0)
+
+    def test_gain(self):
+        waveform_gain = F.gain(self.waveform_train, 3)
+        self.assertTrue(waveform_gain.abs().max().item(), 1.0)
+
+        E = torchaudio.sox_effects.SoxEffectsChain()
+        E.set_input_file(self.test_filepath)
+        E.append_effect_to_chain("gain", [3])
+        sox_gain_waveform = E.sox_build_flow_effects()[0]
+
+        self.assertTrue(torch.allclose(waveform_gain, sox_gain_waveform, atol=1e-04))
+
+    def test_dither(self):
+        waveform_dithered = F.dither(self.waveform_train)
+        waveform_dithered_noiseshaped = F.dither(
+            self.waveform_train, noise_shaping=True
         )
 
-    E = torchaudio.sox_effects.SoxEffectsChain()
-    E.set_input_file(self.test_filepath)
-    E.append_effect_to_chain("dither", [])
-    sox_dither_waveform = E.sox_build_flow_effects()[0]
+        E = torchaudio.sox_effects.SoxEffectsChain()
+        E.set_input_file(self.test_filepath)
+        E.append_effect_to_chain("dither", [])
+        sox_dither_waveform = E.sox_build_flow_effects()[0]
 
-    self.assertTrue(
-        torch.allclose(waveform_dithered, sox_dither_waveform, atol = 1e-04)
+        self.assertTrue(
+            torch.allclose(waveform_dithered, sox_dither_waveform, atol=1e-04)
         )
-    E.clear_chain()
+        E.clear_chain()
 
-    E.append_effect_to_chain("dither", ["-s"])
-    sox_dither_waveform_ns = E.sox_build_flow_effects()[0]
+        E.append_effect_to_chain("dither", ["-s"])
+        sox_dither_waveform_ns = E.sox_build_flow_effects()[0]
 
-    self.assertTrue(
-        torch.allclose(
-            waveform_dithered_noiseshaped, sox_dither_waveform_ns, atol = 1e-02
+        self.assertTrue(
+            torch.allclose(
+                waveform_dithered_noiseshaped, sox_dither_waveform_ns, atol=1e-02
             )
         )
 
-  def test_vctk_transform_pipeline(self):
-    test_filepath_vctk = os.path.join(
-        self.test_dirpath, "assets/VCTK-Corpus/wav48/p224/", "p224_002.wav"
+    def test_vctk_transform_pipeline(self):
+        test_filepath_vctk = os.path.join(
+            self.test_dirpath, "assets/VCTK-Corpus/wav48/p224/", "p224_002.wav"
         )
-    wf_vctk, sr_vctk = torchaudio.load(test_filepath_vctk)
-
-    # rate
-    sample = T.Resample(sr_vctk, 16000, resampling_method = "sinc_interpolation")
-    wf_vctk = sample(wf_vctk)
-    # dither
-    wf_vctk = F.dither(wf_vctk, noise_shaping = True)
-
-    E = torchaudio.sox_effects.SoxEffectsChain()
-    E.set_input_file(test_filepath_vctk)
-    E.append_effect_to_chain("gain", ["-h"])
-    E.append_effect_to_chain("channels", [1])
-    E.append_effect_to_chain("rate", [16000])
-    E.append_effect_to_chain("gain", ["-rh"])
-    E.append_effect_to_chain("dither", ["-s"])
-    wf_vctk_sox = E.sox_build_flow_effects()[0]
-
-    self.assertTrue(torch.allclose(wf_vctk, wf_vctk_sox, rtol = 1e-03, atol = 1e-03))
-
-  def test_pitch(self):
-    test_dirpath, test_dir = common_utils.create_temp_assets_dir()
-    test_filepath_100 = os.path.join(
-        test_dirpath, "assets", "100Hz_44100Hz_16bit_05sec.wav"
+        wf_vctk, sr_vctk = torchaudio.load(test_filepath_vctk)
+
+        # rate
+        sample = T.Resample(sr_vctk, 16000, resampling_method="sinc_interpolation")
+        wf_vctk = sample(wf_vctk)
+        # dither
+        wf_vctk = F.dither(wf_vctk, noise_shaping=True)
+
+        E = torchaudio.sox_effects.SoxEffectsChain()
+        E.set_input_file(test_filepath_vctk)
+        E.append_effect_to_chain("gain", ["-h"])
+        E.append_effect_to_chain("channels", [1])
+        E.append_effect_to_chain("rate", [16000])
+        E.append_effect_to_chain("gain", ["-rh"])
+        E.append_effect_to_chain("dither", ["-s"])
+        wf_vctk_sox = E.sox_build_flow_effects()[0]
+
+        self.assertTrue(torch.allclose(wf_vctk, wf_vctk_sox, rtol=1e-03, atol=1e-03))
+
+    def test_pitch(self):
+        test_dirpath, test_dir = common_utils.create_temp_assets_dir()
+        test_filepath_100 = os.path.join(
+            test_dirpath, "assets", "100Hz_44100Hz_16bit_05sec.wav"
         )
-    test_filepath_440 = os.path.join(
-        test_dirpath, "assets", "440Hz_44100Hz_16bit_05sec.wav"
+        test_filepath_440 = os.path.join(
+            test_dirpath, "assets", "440Hz_44100Hz_16bit_05sec.wav"
         )
 
-    # Files from https://www.mediacollege.com/audio/tone/download/
-    tests = [
-        (test_filepath_100, 100),
-        (test_filepath_440, 440),
+        # Files from https://www.mediacollege.com/audio/tone/download/
+        tests = [
+            (test_filepath_100, 100),
+            (test_filepath_440, 440),
         ]
 
-    for filename, freq_ref in tests:
-      waveform, sample_rate = torchaudio.load(filename)
+        for filename, freq_ref in tests:
+            waveform, sample_rate = torchaudio.load(filename)
 
-      freq = torchaudio.functional.detect_pitch_frequency(waveform, sample_rate)
+            freq = torchaudio.functional.detect_pitch_frequency(waveform, sample_rate)
 
-      threshold = 1
-      s = ((freq - freq_ref).abs() > threshold).sum()
-      self.assertFalse(s)
+            threshold = 1
+            s = ((freq - freq_ref).abs() > threshold).sum()
+            self.assertFalse(s)
 
-      # Convert to stereo and batch for testing purposes
-      self._test_batch(F.detect_pitch_frequency, waveform, sample_rate)
+            # Convert to stereo and batch for testing purposes
+            self._test_batch(F.detect_pitch_frequency, waveform, sample_rate)
 
-  def _test_batch_shape(self, functional, tensor, *args, **kwargs):
+    def _test_batch_shape(self, functional, tensor, *args, **kwargs):
 
-    kwargs_compare = {}
-    if "atol" in kwargs:
-      atol = kwargs["atol"]
-      del kwargs["atol"]
-      kwargs_compare["atol"] = atol
+        kwargs_compare = {}
+        if "atol" in kwargs:
+            atol = kwargs["atol"]
+            del kwargs["atol"]
+            kwargs_compare["atol"] = atol
 
-    if "rtol" in kwargs:
-      rtol = kwargs["rtol"]
-      del kwargs["rtol"]
-      kwargs_compare["rtol"] = rtol
+        if "rtol" in kwargs:
+            rtol = kwargs["rtol"]
+            del kwargs["rtol"]
+            kwargs_compare["rtol"] = rtol
 
-    # Single then transform then batch
+        # Single then transform then batch
 
-    torch.random.manual_seed(42)
-    expected = functional(tensor.clone(), *args, **kwargs)
-    expected = expected.unsqueeze(0).unsqueeze(0)
+        torch.random.manual_seed(42)
+        expected = functional(tensor.clone(), *args, **kwargs)
+        expected = expected.unsqueeze(0).unsqueeze(0)
 
-    # 1-Batch then transform
+        # 1-Batch then transform
 
-    tensors = tensor.unsqueeze(0).unsqueeze(0)
+        tensors = tensor.unsqueeze(0).unsqueeze(0)
 
-    torch.random.manual_seed(42)
-    computed = functional(tensors.clone(), *args, **kwargs)
+        torch.random.manual_seed(42)
+        computed = functional(tensors.clone(), *args, **kwargs)
 
-    self._compare_estimate(computed, expected, **kwargs_compare)
+        self._compare_estimate(computed, expected, **kwargs_compare)
 
-    return tensors, expected
+        return tensors, expected
 
-  def _test_batch(self, functional, tensor, *args, **kwargs):
+    def _test_batch(self, functional, tensor, *args, **kwargs):
 
-    tensors, expected = self._test_batch_shape(functional, tensor, *args, **kwargs)
+        tensors, expected = self._test_batch_shape(functional, tensor, *args, **kwargs)
 
-    kwargs_compare = {}
-    if "atol" in kwargs:
-      atol = kwargs["atol"]
-      del kwargs["atol"]
-      kwargs_compare["atol"] = atol
+        kwargs_compare = {}
+        if "atol" in kwargs:
+            atol = kwargs["atol"]
+            del kwargs["atol"]
+            kwargs_compare["atol"] = atol
 
-    if "rtol" in kwargs:
-      rtol = kwargs["rtol"]
-      del kwargs["rtol"]
-      kwargs_compare["rtol"] = rtol
+        if "rtol" in kwargs:
+            rtol = kwargs["rtol"]
+            del kwargs["rtol"]
+            kwargs_compare["rtol"] = rtol
 
-    # 3-Batch then transform
+        # 3-Batch then transform
 
-    ind = [3] + [1] * (int(tensors.dim()) - 1)
-    tensors = tensor.repeat(*ind)
+        ind = [3] + [1] * (int(tensors.dim()) - 1)
+        tensors = tensor.repeat(*ind)
 
-    ind = [3] + [1] * (int(expected.dim()) - 1)
-    expected = expected.repeat(*ind)
+        ind = [3] + [1] * (int(expected.dim()) - 1)
+        expected = expected.repeat(*ind)
 
-    torch.random.manual_seed(42)
-    computed = functional(tensors.clone(), *args, **kwargs)
+        torch.random.manual_seed(42)
+        computed = functional(tensors.clone(), *args, **kwargs)
 
-  def test_torchscript_create_fb_matrix(self):
+    def test_torchscript_create_fb_matrix(self):
 
-    n_stft = 100
-    f_min = 0.0
-    f_max = 20.0
-    n_mels = 10
-    sample_rate = 16000
+        n_stft = 100
+        f_min = 0.0
+        f_max = 20.0
+        n_mels = 10
+        sample_rate = 16000
 
-    _test_torchscript_functional(
-        F.create_fb_matrix, n_stft, f_min, f_max, n_mels, sample_rate
+        _test_torchscript_functional(
+            F.create_fb_matrix, n_stft, f_min, f_max, n_mels, sample_rate
         )
 
-  def test_torchscript_amplitude_to_DB(self):
-    spec = torch.rand((6, 201))
-    multiplier = 10.0
-    amin = 1e-10
-    db_multiplier = 0.0
-    top_db = 80.0
+    def test_torchscript_amplitude_to_DB(self):
+        spec = torch.rand((6, 201))
+        multiplier = 10.0
+        amin = 1e-10
+        db_multiplier = 0.0
+        top_db = 80.0
 
-    _test_torchscript_functional(
-        F.amplitude_to_DB, spec, multiplier, amin, db_multiplier, top_db
+        _test_torchscript_functional(
+            F.amplitude_to_DB, spec, multiplier, amin, db_multiplier, top_db
         )
 
-  def test_torchscript_DB_to_amplitude(self):
+    def test_torchscript_DB_to_amplitude(self):
 
-    x = torch.rand((1, 100))
-    ref = 1.0
-    power = 1.0
+        x = torch.rand((1, 100))
+        ref = 1.0
+        power = 1.0
 
-    _test_torchscript_functional(F.DB_to_amplitude, x, ref, power)
+        _test_torchscript_functional(F.DB_to_amplitude, x, ref, power)
 
-  def test_DB_to_amplitude(self):
-    # Make some noise
-    x = torch.rand(1000)
-    spectrogram = torchaudio.transforms.Spectrogram()
-    spec = spectrogram(x)
+    def test_DB_to_amplitude(self):
+        # Make some noise
+        x = torch.rand(1000)
+        spectrogram = torchaudio.transforms.Spectrogram()
+        spec = spectrogram(x)
 
-    amin = 1e-10
-    ref = 1.0
-    db_multiplier = math.log10(max(amin, ref))
+        amin = 1e-10
+        ref = 1.0
+        db_multiplier = math.log10(max(amin, ref))
 
-    # Waveform amplitude -> DB -> amplitude
-    multiplier = 20.0
-    power = 0.5
+        # Waveform amplitude -> DB -> amplitude
+        multiplier = 20.0
+        power = 0.5
 
-    db = F.amplitude_to_DB(
-        torch.abs(x), multiplier, amin, db_multiplier, top_db = None
+        db = F.amplitude_to_DB(
+            torch.abs(x), multiplier, amin, db_multiplier, top_db=None
         )
-    x2 = F.DB_to_amplitude(db, ref, power)
+        x2 = F.DB_to_amplitude(db, ref, power)
 
-    self.assertTrue(torch.allclose(torch.abs(x), x2, atol = 5e-5))
+        self.assertTrue(torch.allclose(torch.abs(x), x2, atol=5e-5))
 
-    # Spectrogram amplitude -> DB -> amplitude
-    db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db = None)
-    x2 = F.DB_to_amplitude(db, ref, power)
+        # Spectrogram amplitude -> DB -> amplitude
+        db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None)
+        x2 = F.DB_to_amplitude(db, ref, power)
 
-    self.assertTrue(torch.allclose(spec, x2, atol = 5e-5))
+        self.assertTrue(torch.allclose(spec, x2, atol=5e-5))
 
-    # Waveform power -> DB -> power
-    multiplier = 10.0
-    power = 1.0
+        # Waveform power -> DB -> power
+        multiplier = 10.0
+        power = 1.0
 
-    db = F.amplitude_to_DB(x, multiplier, amin, db_multiplier, top_db = None)
-    x2 = F.DB_to_amplitude(db, ref, power)
+        db = F.amplitude_to_DB(x, multiplier, amin, db_multiplier, top_db=None)
+        x2 = F.DB_to_amplitude(db, ref, power)
 
-    self.assertTrue(torch.allclose(torch.abs(x), x2, atol = 5e-5))
+        self.assertTrue(torch.allclose(torch.abs(x), x2, atol=5e-5))
 
-    # Spectrogram power -> DB -> power
-    db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db = None)
-    x2 = F.DB_to_amplitude(db, ref, power)
+        # Spectrogram power -> DB -> power
+        db = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db=None)
+        x2 = F.DB_to_amplitude(db, ref, power)
 
-    self.assertTrue(torch.allclose(spec, x2, atol = 5e-5))
+        self.assertTrue(torch.allclose(spec, x2, atol=5e-5))
 
-  def test_torchscript_create_dct(self):
+    def test_torchscript_create_dct(self):
 
-    n_mfcc = 40
-    n_mels = 128
-    norm = "ortho"
+        n_mfcc = 40
+        n_mels = 128
+        norm = "ortho"
 
-    _test_torchscript_functional(F.create_dct, n_mfcc, n_mels, norm)
+        _test_torchscript_functional(F.create_dct, n_mfcc, n_mels, norm)
 
-  def test_torchscript_mu_law_encoding(self):
+    def test_torchscript_mu_law_encoding(self):
 
-    tensor = torch.rand((1, 10))
-    qc = 256
+        tensor = torch.rand((1, 10))
+        qc = 256
 
-    _test_torchscript_functional(F.mu_law_encoding, tensor, qc)
+        _test_torchscript_functional(F.mu_law_encoding, tensor, qc)
 
-  def test_torchscript_mu_law_decoding(self):
+    def test_torchscript_mu_law_decoding(self):
 
-    tensor = torch.rand((1, 10))
-    qc = 256
+        tensor = torch.rand((1, 10))
+        qc = 256
 
-    _test_torchscript_functional(F.mu_law_decoding, tensor, qc)
+        _test_torchscript_functional(F.mu_law_decoding, tensor, qc)
 
-  def test_torchscript_complex_norm(self):
+    def test_torchscript_complex_norm(self):
 
-    complex_tensor = torch.randn(1, 2, 1025, 400, 2)
-    power = 2
+        complex_tensor = torch.randn(1, 2, 1025, 400, 2)
+        power = 2
 
-    _test_torchscript_functional(F.complex_norm, complex_tensor, power)
+        _test_torchscript_functional(F.complex_norm, complex_tensor, power)
 
-  def test_mask_along_axis(self):
+    def test_mask_along_axis(self):
 
-    specgram = torch.randn(2, 1025, 400)
-    mask_param = 100
-    mask_value = 30.0
-    axis = 2
+        specgram = torch.randn(2, 1025, 400)
+        mask_param = 100
+        mask_value = 30.0
+        axis = 2
 
-    _test_torchscript_functional(
-        F.mask_along_axis, specgram, mask_param, mask_value, axis
+        _test_torchscript_functional(
+            F.mask_along_axis, specgram, mask_param, mask_value, axis
         )
 
-  def test_mask_along_axis_iid(self):
+    def test_mask_along_axis_iid(self):
 
-    specgrams = torch.randn(4, 2, 1025, 400)
-    mask_param = 100
-    mask_value = 30.0
-    axis = 2
+        specgrams = torch.randn(4, 2, 1025, 400)
+        mask_param = 100
+        mask_value = 30.0
+        axis = 2
 
-    _test_torchscript_functional(
-        F.mask_along_axis_iid, specgrams, mask_param, mask_value, axis
+        _test_torchscript_functional(
+            F.mask_along_axis_iid, specgrams, mask_param, mask_value, axis
         )
 
-  def test_torchscript_gain(self):
-    tensor = torch.rand((1, 1000))
-    gainDB = 2.0
+    def test_torchscript_gain(self):
+        tensor = torch.rand((1, 1000))
+        gainDB = 2.0
 
-    _test_torchscript_functional(F.gain, tensor, gainDB)
+        _test_torchscript_functional(F.gain, tensor, gainDB)
 
-  def test_torchscript_dither(self):
-    tensor = torch.rand((2, 1000))
+    def test_torchscript_dither(self):
+        tensor = torch.rand((2, 1000))
 
-    _test_torchscript_functional_shape(F.dither, tensor)
-    _test_torchscript_functional_shape(F.dither, tensor, "RPDF")
-    _test_torchscript_functional_shape(F.dither, tensor, "GPDF")
+        _test_torchscript_functional_shape(F.dither, tensor)
+        _test_torchscript_functional_shape(F.dither, tensor, "RPDF")
+        _test_torchscript_functional_shape(F.dither, tensor, "GPDF")
 
 
 def _num_stft_bins(signal_len, fft_len, hop_length, pad):
-  return (signal_len + 2 * pad - fft_len + hop_length) // hop_length
+    return (signal_len + 2 * pad - fft_len + hop_length) // hop_length
 
 
 @pytest.mark.parametrize("complex_specgrams", [torch.randn(2, 1025, 400, 2)])
 @pytest.mark.parametrize("rate", [0.5, 1.01, 1.3])
 @pytest.mark.parametrize("hop_length", [256])
 def test_phase_vocoder(complex_specgrams, rate, hop_length):
-  # Due to cummulative sum, numerical error in using torch.float32 will
-  # result in bottom right values of the stretched sectrogram to not
-  # match with librosa.
+    # Due to cummulative sum, numerical error in using torch.float32 will
+    # result in bottom right values of the stretched sectrogram to not
+    # match with librosa.
 
-  complex_specgrams = complex_specgrams.type(torch.float64)
-  phase_advance = torch.linspace(
-      0, numpy.pi * hop_length, complex_specgrams.shape[-3], dtype = torch.float64
-      )[..., None]
+    complex_specgrams = complex_specgrams.type(torch.float64)
+    phase_advance = torch.linspace(
+        0, numpy.pi * hop_length, complex_specgrams.shape[-3], dtype=torch.float64
+    )[..., None]
 
-  complex_specgrams_stretch = F.phase_vocoder(
-      complex_specgrams, rate = rate, phase_advance = phase_advance
-      )
+    complex_specgrams_stretch = F.phase_vocoder(
+        complex_specgrams, rate=rate, phase_advance=phase_advance
+    )
 
-  # == Test shape
-  expected_size = list(complex_specgrams.size())
-  expected_size[-2] = int(numpy.ceil(expected_size[-2] / rate))
+    # == Test shape
+    expected_size = list(complex_specgrams.size())
+    expected_size[-2] = int(numpy.ceil(expected_size[-2] / rate))
 
-  assert complex_specgrams.dim() == complex_specgrams_stretch.dim()
-  assert complex_specgrams_stretch.size() == torch.Size(expected_size)
+    assert complex_specgrams.dim() == complex_specgrams_stretch.dim()
+    assert complex_specgrams_stretch.size() == torch.Size(expected_size)
 
-  # == Test values
-  index = [0] * (complex_specgrams.dim() - 3) + [slice(None)] * 3
-  mono_complex_specgram = complex_specgrams[index].numpy()
-  mono_complex_specgram = (
-      mono_complex_specgram[..., 0] + mono_complex_specgram[..., 1] * 1j
-  )
-  expected_complex_stretch = librosa.phase_vocoder(
-      mono_complex_specgram, rate = rate, hop_length = hop_length
-      )
+    # == Test values
+    index = [0] * (complex_specgrams.dim() - 3) + [slice(None)] * 3
+    mono_complex_specgram = complex_specgrams[index].numpy()
+    mono_complex_specgram = (
+        mono_complex_specgram[..., 0] + mono_complex_specgram[..., 1] * 1j
+    )
+    expected_complex_stretch = librosa.phase_vocoder(
+        mono_complex_specgram, rate=rate, hop_length=hop_length
+    )
 
-  complex_stretch = complex_specgrams_stretch[index].numpy()
-  complex_stretch = complex_stretch[..., 0] + 1j * complex_stretch[..., 1]
+    complex_stretch = complex_specgrams_stretch[index].numpy()
+    complex_stretch = complex_stretch[..., 0] + 1j * complex_stretch[..., 1]
 
-  assert numpy.allclose(complex_stretch, expected_complex_stretch, atol = 1e-5)
+    assert numpy.allclose(complex_stretch, expected_complex_stretch, atol=1e-5)
 
 
 @pytest.mark.parametrize(
     "complex_tensor", [torch.randn(1, 2, 1025, 400, 2), torch.randn(1025, 400, 2)]
-    )
+)
 @pytest.mark.parametrize("power", [1, 2, 0.7])
 def test_complex_norm(complex_tensor, power):
-  expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2)
-  norm_tensor = F.complex_norm(complex_tensor, power)
+    expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2)
+    norm_tensor = F.complex_norm(complex_tensor, power)
 
-  assert torch.allclose(expected_norm_tensor, norm_tensor, atol = 1e-5)
+    assert torch.allclose(expected_norm_tensor, norm_tensor, atol=1e-5)
 
 
 @pytest.mark.parametrize(
     "specgram", [torch.randn(2, 1025, 400), torch.randn(1, 201, 100)]
-    )
+)
 @pytest.mark.parametrize("mask_param", [100])
 @pytest.mark.parametrize("mask_value", [0.0, 30.0])
 @pytest.mark.parametrize("axis", [1, 2])
 def test_mask_along_axis(specgram, mask_param, mask_value, axis):
-  mask_specgram = F.mask_along_axis(specgram, mask_param, mask_value, axis)
+    mask_specgram = F.mask_along_axis(specgram, mask_param, mask_value, axis)
 
-  other_axis = 1 if axis == 2 else 2
+    other_axis = 1 if axis == 2 else 2
 
-  masked_columns = (mask_specgram == mask_value).sum(other_axis)
-  num_masked_columns = (masked_columns == mask_specgram.size(other_axis)).sum()
-  num_masked_columns /= mask_specgram.size(0)
+    masked_columns = (mask_specgram == mask_value).sum(other_axis)
+    num_masked_columns = (masked_columns == mask_specgram.size(other_axis)).sum()
+    num_masked_columns /= mask_specgram.size(0)
 
-  assert mask_specgram.size() == specgram.size()
-  assert num_masked_columns < mask_param
+    assert mask_specgram.size() == specgram.size()
+    assert num_masked_columns < mask_param
 
 
 @pytest.mark.parametrize(
     "specgrams",
     [
         torch.randn(4, 2, 1025, 400),
-        ],
-    )
+    ],
+)
 @pytest.mark.parametrize("mask_param", [100])
 @pytest.mark.parametrize("mask_value", [0.0, 30.0])
 @pytest.mark.parametrize("axis", [2, 3])
 def test_mask_along_axis_iid(specgrams, mask_param, mask_value, axis):
-  mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis)
+    mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis)
 
-  other_axis = 2 if axis == 3 else 3
+    other_axis = 2 if axis == 3 else 3
 
-  masked_columns = (mask_specgrams == mask_value).sum(other_axis)
-  num_masked_columns = (masked_columns == mask_specgrams.size(other_axis)).sum(-1)
+    masked_columns = (mask_specgrams == mask_value).sum(other_axis)
+    num_masked_columns = (masked_columns == mask_specgrams.size(other_axis)).sum(-1)
 
-  assert mask_specgrams.size() == specgrams.size()
-  assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel()
+    assert mask_specgrams.size() == specgrams.size()
+    assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel()
 
 
 if __name__ == "__main__":
-  unittest.main()
+    unittest.main()