import submodules

modulation/classification/architecture/__init__.py

@@ -8,3 +8,4 @@
            """
 
 from .m5 import *
+from .cepstral_a import *

modulation/classification/metrics/__init__.py

@@ -6,3 +6,6 @@
 
            Created on 13-01-2021
            """
+
+
+from .lwlrap import *

modulation/classification/seperation/__init__.py

@@ -6,3 +6,5 @@
 
            Created on 18-12-2020
            """
+
+from .audio_unet import *

modulation/data/__init__.py

@@ -2,6 +2,10 @@
 # -*- coding: utf-8 -*-
 
 __author__ = "Christian Heider Nielsen"
-__doc__ = """ description """
+__doc__ = r"""
 
-from modulation.data.audio.speech import *
+           Created on 04-05-2021
+           """
+
+from .audio import *
+from .signal_generator import *

modulation/data/audio/__init__.py

@@ -0,0 +1,10 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+__author__ = "Christian Heider Nielsen"
+__doc__ = r"""
+
+           Created on 04-05-2021
+           """
+
+from .speech import *

modulation/data/audio/speech/__init__.py

@@ -0,0 +1,11 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+__author__ = "Christian Heider Nielsen"
+__doc__ = r"""
+
+           Created on 04-05-2021
+           """
+
+from .seperation import *
+from .recognition import *

modulation/data/audio/speech/recognition/libri_speech.py

@@ -11,15 +11,18 @@
 from enum import Enum
 from pathlib import Path
 
-from draugr import FilterModeEnum, symbol_filter
 from draugr.numpy_utilities import SplitEnum, SplitIndexer
 from torchaudio.datasets import LIBRISPEECH
+from warg import FilterModeEnum, symbol_filter
 
 
 class LibriSpeech(LIBRISPEECH):
     """description"""
 
     class LibriSpeechSubsets(Enum):
+        '''
+
+        '''
         dev_clean = "dev-clean"
         dev_other = "dev-other"
         test_clean = "test-clean"
@@ -29,6 +32,9 @@ class LibriSpeechSubsets(Enum):
         train_other_500 = "train-other-500"
 
     class CustomSubsets(Enum):
+        '''
+
+        '''
         male = "M"
         female = "F"
         # TODO: add more like Splits ...

modulation/data/audio/speech/seperation/__init__.py

@@ -6,3 +6,5 @@
 
            Created on 04-05-2021
            """
+
+from .dsd100 import *

modulation/data/signal_generator.py

@@ -16,96 +16,97 @@
 __all__ = ["SignalGenerator", "multifreq", "triangle", "sawtooth"]
 
 
-def multifreq(x) -> 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)
-    )
-
-
-def triangle(x, section_length=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
-
-
-def sawtooth(x, section_length=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
+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)
+  )
+
+
+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
+
+
+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
 
 
 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()

modulation/regression/denoising/__init__.py

@@ -6,3 +6,7 @@
 
            Created on 02-12-2020
            """
+
+
+from .band_pass import *
+from .finger_print import *

modulation/regression/linear_predictive_coding.py

@@ -9,9 +9,13 @@
 
 import numpy
 
+__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"
+  '''
+  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)]

modulation/regression/spectral_denoise.py

@@ -10,6 +10,8 @@
 import numpy
 
 
+__all__=['fft_denoise']
+
 def fft_denoise(
     noisy_signal: numpy.ndarray, num_time_steps: int, cutoff_threshold: float = 100
     ) -> numpy.ndarray:

modulation/signal_utilities/__init__.py

@@ -10,3 +10,5 @@
 from .chopping import *
 from .segmentation import *
 from .splitting import *
+from .audio_utilities import *
+from .noise_generation import *

modulation/signal_utilities/audio_utilities/__init__.py

@@ -6,3 +6,7 @@
 
            Created on 11-01-2021
            """
+
+
+from .persistence import *
+from .filtering import *

modulation/signal_utilities/audio_utilities/filtering.py

@@ -12,6 +12,7 @@
 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"]
 
 def mel_scale(x: numpy.ndarray) -> numpy.ndarray:
     """

modulation/signal_utilities/audio_utilities/persistence.py

@@ -13,6 +13,7 @@
 import numpy
 import soundfile
 
+__all__=['audio_read','audio_write']
 
 def audio_read(
     path: str, norm: bool = True, start: int = 0, stop: int = None
@@ -81,3 +82,7 @@ def audio_write(
         os.makedirs(destdir)
 
     soundfile.write(dest_path, data, fs)
+
+
+if __name__ == '__main__':
+    pass

modulation/signal_utilities/noise_generation/__init__.py

@@ -9,3 +9,5 @@
 
 from .babble_noise import *
 from .shaped_noise import *
+from .gaussian_noise import *
+from .additive_noise import *

modulation/signal_utilities/noise_generation/additive_noise.py

@@ -20,6 +20,7 @@
 
 from modulation.signal_utilities import mask_split_non_zero_concat
 
+__all__ = ['sample_noise','compute_additive_noise_samples']
 
 def sample_noise(
     noise,

modulation/signal_utilities/noise_generation/babble_noise.py

@@ -25,6 +25,7 @@ def generate_babble_noise(
     export_path: Path = None,
 ) -> Iterable:
     """
+    Generates babble noise from samples
 
     :param samples:
     :type samples: