merge_music_speech.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "641\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 1\n",
      "[[32  0  1  0  2  0  0  0  0  0]\n",
      " [ 0 32  1  1  0  0  0  0  1  0]\n",
      " [ 1  0 21  2  2  0  0  2  3  5]\n",
      " [ 0  0  2 21  4  0  2  3  0  5]\n",
      " [ 0  0  0  1 20  0  0  0  7  1]\n",
      " [ 1  3  4  0  1 26  0  0  2  0]\n",
      " [ 3  0  0  2  3  1 25  0  0  4]\n",
      " [ 0  0  0  1  3  0  0 25  1  2]\n",
      " [ 1  0  1  0 10  0  0  2 29  4]\n",
      " [ 1  0  1  5  2  0  0  2  3 19]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.82      0.91      0.86        35\n",
      "           2       0.91      0.91      0.91        35\n",
      "           3       0.68      0.58      0.63        36\n",
      "           4       0.64      0.57      0.60        37\n",
      "           5       0.43      0.69      0.53        29\n",
      "           6       0.96      0.70      0.81        37\n",
      "           7       0.93      0.66      0.77        38\n",
      "           8       0.74      0.78      0.76        32\n",
      "           9       0.63      0.62      0.62        47\n",
      "          10       0.47      0.58      0.52        33\n",
      "\n",
      "    accuracy                           0.70       359\n",
      "   macro avg       0.72      0.70      0.70       359\n",
      "weighted avg       0.73      0.70      0.70       359\n",
      "\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 2\n",
      "[[32  0  1  0  2  0  0  0  0  0]\n",
      " [ 0 32  1  1  0  0  0  0  1  0]\n",
      " [ 1  0 21  2  2  0  0  2  3  5]\n",
      " [ 0  0  2 21  4  0  2  3  0  5]\n",
      " [ 0  0  0  1 20  0  0  0  7  1]\n",
      " [ 1  3  4  0  1 26  0  0  2  0]\n",
      " [ 3  0  0  2  3  1 25  0  0  4]\n",
      " [ 0  0  0  1  3  0  0 25  1  2]\n",
      " [ 1  0  1  0 10  0  0  2 29  4]\n",
      " [ 1  0  1  5  2  0  0  2  3 19]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.82      0.91      0.86        35\n",
      "           2       0.91      0.91      0.91        35\n",
      "           3       0.68      0.58      0.63        36\n",
      "           4       0.64      0.57      0.60        37\n",
      "           5       0.43      0.69      0.53        29\n",
      "           6       0.96      0.70      0.81        37\n",
      "           7       0.93      0.66      0.77        38\n",
      "           8       0.74      0.78      0.76        32\n",
      "           9       0.63      0.62      0.62        47\n",
      "          10       0.47      0.58      0.52        33\n",
      "\n",
      "    accuracy                           0.70       359\n",
      "   macro avg       0.72      0.70      0.70       359\n",
      "weighted avg       0.73      0.70      0.70       359\n",
      "\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 3\n",
      "[[31  0  0  0  1  0  0  0  3  0]\n",
      " [ 0 31  1  1  0  1  0  0  1  0]\n",
      " [ 1  0 21  3  2  0  0  3  3  3]\n",
      " [ 0  0  2 18  5  0  2  6  1  3]\n",
      " [ 0  0  0  1 22  0  0  2  4  0]\n",
      " [ 1  4  4  1  1 25  0  0  1  0]\n",
      " [ 1  0  0  2  5  1 24  0  0  5]\n",
      " [ 0  0  0  1  2  0  0 26  1  2]\n",
      " [ 1  0  1  2  8  0  0  2 30  3]\n",
      " [ 0  0  2  6  2  0  0  1  4 18]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.89      0.89      0.89        35\n",
      "           2       0.89      0.89      0.89        35\n",
      "           3       0.68      0.58      0.63        36\n",
      "           4       0.51      0.49      0.50        37\n",
      "           5       0.46      0.76      0.57        29\n",
      "           6       0.93      0.68      0.78        37\n",
      "           7       0.92      0.63      0.75        38\n",
      "           8       0.65      0.81      0.72        32\n",
      "           9       0.62      0.64      0.63        47\n",
      "          10       0.53      0.55      0.54        33\n",
      "\n",
      "    accuracy                           0.69       359\n",
      "   macro avg       0.71      0.69      0.69       359\n",
      "weighted avg       0.71      0.69      0.69       359\n",
      "\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 4\n",
      "[[30  0  2  0  0  0  0  0  3  0]\n",
      " [ 0 32  1  1  0  0  0  0  1  0]\n",
      " [ 1  0 22  3  1  0  0  3  4  2]\n",
      " [ 0  0  2 21  5  0  1  6  1  1]\n",
      " [ 0  0  0  1 20  0  0  2  6  0]\n",
      " [ 0  5  3  1  1 26  0  0  1  0]\n",
      " [ 1  0  0  3  4  1 24  0  0  5]\n",
      " [ 0  0  0  1  2  0  0 25  2  2]\n",
      " [ 1  0  1  1 10  0  0  2 30  2]\n",
      " [ 0  0  2  6  2  0  0  1  3 19]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.91      0.86      0.88        35\n",
      "           2       0.86      0.91      0.89        35\n",
      "           3       0.67      0.61      0.64        36\n",
      "           4       0.55      0.57      0.56        37\n",
      "           5       0.44      0.69      0.54        29\n",
      "           6       0.96      0.70      0.81        37\n",
      "           7       0.96      0.63      0.76        38\n",
      "           8       0.64      0.78      0.70        32\n",
      "           9       0.59      0.64      0.61        47\n",
      "          10       0.61      0.58      0.59        33\n",
      "\n",
      "    accuracy                           0.69       359\n",
      "   macro avg       0.72      0.70      0.70       359\n",
      "weighted avg       0.72      0.69      0.70       359\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 5\n",
      "[[29  0  2  0  0  0  0  0  4  0]\n",
      " [ 0 32  1  1  0  0  0  0  1  0]\n",
      " [ 1  0 23  1  1  0  0  3  4  3]\n",
      " [ 0  0  2 20  4  0  1  5  1  4]\n",
      " [ 0  0  0  1 20  0  0  3  4  1]\n",
      " [ 1  5  3  1  1 25  0  0  1  0]\n",
      " [ 1  0  0  4  3  1 24  0  0  5]\n",
      " [ 0  0  0  0  2  0  0 26  2  2]\n",
      " [ 0  0  0  2 11  0  0  3 30  1]\n",
      " [ 0  0  2  6  3  0  0  2  3 17]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.91      0.83      0.87        35\n",
      "           2       0.86      0.91      0.89        35\n",
      "           3       0.70      0.64      0.67        36\n",
      "           4       0.56      0.54      0.55        37\n",
      "           5       0.44      0.69      0.54        29\n",
      "           6       0.96      0.68      0.79        37\n",
      "           7       0.96      0.63      0.76        38\n",
      "           8       0.62      0.81      0.70        32\n",
      "           9       0.60      0.64      0.62        47\n",
      "          10       0.52      0.52      0.52        33\n",
      "\n",
      "    accuracy                           0.69       359\n",
      "   macro avg       0.71      0.69      0.69       359\n",
      "weighted avg       0.72      0.69      0.69       359\n",
      "\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 6\n",
      "[[30  0  1  0  0  0  0  0  4  0]\n",
      " [ 0 32  1  0  0  0  0  0  1  1]\n",
      " [ 1  0 24  2  1  0  0  3  3  2]\n",
      " [ 0  0  1 23  3  0  1  5  1  3]\n",
      " [ 0  0  0  1 21  0  0  3  4  0]\n",
      " [ 0  4  4  1  1 26  0  0  1  0]\n",
      " [ 1  0  0  3  5  1 24  0  0  4]\n",
      " [ 0  0  0  1  2  0  0 25  2  2]\n",
      " [ 0  0  0  2  9  0  0  3 32  1]\n",
      " [ 0  0  2  6  4  0  0  1  3 17]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.94      0.86      0.90        35\n",
      "           2       0.89      0.91      0.90        35\n",
      "           3       0.73      0.67      0.70        36\n",
      "           4       0.59      0.62      0.61        37\n",
      "           5       0.46      0.72      0.56        29\n",
      "           6       0.96      0.70      0.81        37\n",
      "           7       0.96      0.63      0.76        38\n",
      "           8       0.62      0.78      0.69        32\n",
      "           9       0.63      0.68      0.65        47\n",
      "          10       0.57      0.52      0.54        33\n",
      "\n",
      "    accuracy                           0.71       359\n",
      "   macro avg       0.73      0.71      0.71       359\n",
      "weighted avg       0.74      0.71      0.71       359\n",
      "\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 7\n",
      "[[30  0  2  0  0  0  0  0  3  0]\n",
      " [ 0 32  1  0  0  0  0  0  1  1]\n",
      " [ 1  0 24  2  1  0  0  3  3  2]\n",
      " [ 0  0  1 19  7  0  1  6  1  2]\n",
      " [ 0  0  0  1 21  0  0  3  4  0]\n",
      " [ 1  5  1  1  1 26  0  0  1  1]\n",
      " [ 1  0  0  4  4  1 23  0  0  5]\n",
      " [ 0  0  0  1  2  0  0 25  2  2]\n",
      " [ 0  0  1  2  7  0  0  5 31  1]\n",
      " [ 0  0  2  8  4  0  0  1  3 15]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.91      0.86      0.88        35\n",
      "           2       0.86      0.91      0.89        35\n",
      "           3       0.75      0.67      0.71        36\n",
      "           4       0.50      0.51      0.51        37\n",
      "           5       0.45      0.72      0.55        29\n",
      "           6       0.96      0.70      0.81        37\n",
      "           7       0.96      0.61      0.74        38\n",
      "           8       0.58      0.78      0.67        32\n",
      "           9       0.63      0.66      0.65        47\n",
      "          10       0.52      0.45      0.48        33\n",
      "\n",
      "    accuracy                           0.69       359\n",
      "   macro avg       0.71      0.69      0.69       359\n",
      "weighted avg       0.72      0.69      0.69       359\n",
      "\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 8\n",
      "[[28  0  3  0  0  0  0  0  4  0]\n",
      " [ 0 32  1  0  0  0  0  0  1  1]\n",
      " [ 1  0 21  2  0  0  0  3  5  4]\n",
      " [ 0  0  1 23  3  0  1  7  0  2]\n",
      " [ 0  0  0  1 19  0  0  3  6  0]\n",
      " [ 1  6  2  1  1 25  0  0  0  1]\n",
      " [ 1  0  0  4  5  1 20  0  0  7]\n",
      " [ 0  0  0  2  1  0  0 26  2  1]\n",
      " [ 0  0  0  2 10  0  0  4 30  1]\n",
      " [ 0  0  3  8  2  0  0  0  3 17]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.90      0.80      0.85        35\n",
      "           2       0.84      0.91      0.88        35\n",
      "           3       0.68      0.58      0.63        36\n",
      "           4       0.53      0.62      0.57        37\n",
      "           5       0.46      0.66      0.54        29\n",
      "           6       0.96      0.68      0.79        37\n",
      "           7       0.95      0.53      0.68        38\n",
      "           8       0.60      0.81      0.69        32\n",
      "           9       0.59      0.64      0.61        47\n",
      "          10       0.50      0.52      0.51        33\n",
      "\n",
      "    accuracy                           0.67       359\n",
      "   macro avg       0.70      0.67      0.68       359\n",
      "weighted avg       0.71      0.67      0.68       359\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%run music_genre_classification.ipynb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: librosa in c:\\users\\swetha\\anaconda3\\lib\\site-packages (0.8.0)\n",
      "Requirement already satisfied: decorator>=3.0.0 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from librosa) (4.4.2)\n",
      "Requirement already satisfied: joblib>=0.14 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from librosa) (0.17.0)\n",
      "Requirement already satisfied: numba>=0.43.0 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from librosa) (0.51.2)\n",
      "Requirement already satisfied: audioread>=2.0.0 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from librosa) (2.1.9)\n",
      "Requirement already satisfied: pooch>=1.0 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from librosa) (1.3.0)\n",
      "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from librosa) (0.23.2)\n",
      "Requirement already satisfied: resampy>=0.2.2 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from librosa) (0.2.2)\n",
      "Requirement already satisfied: numpy>=1.15.0 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from librosa) (1.19.2)\n",
      "Requirement already satisfied: scipy>=1.0.0 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from librosa) (1.5.2)\n",
      "Requirement already satisfied: soundfile>=0.9.0 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from librosa) (0.10.3.post1)\n",
      "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from numba>=0.43.0->librosa) (0.34.0)\n",
      "Requirement already satisfied: setuptools in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from numba>=0.43.0->librosa) (50.3.1.post20201107)\n",
      "Requirement already satisfied: requests in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from pooch>=1.0->librosa) (2.24.0)\n",
      "Requirement already satisfied: packaging in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from pooch>=1.0->librosa) (20.4)\n",
      "Requirement already satisfied: appdirs in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from pooch>=1.0->librosa) (1.4.4)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from scikit-learn!=0.19.0,>=0.14.0->librosa) (2.1.0)\n",
      "Requirement already satisfied: six>=1.3 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from resampy>=0.2.2->librosa) (1.15.0)\n",
      "Requirement already satisfied: cffi>=1.0 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from soundfile>=0.9.0->librosa) (1.14.3)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from requests->pooch>=1.0->librosa) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from requests->pooch>=1.0->librosa) (2020.6.20)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from requests->pooch>=1.0->librosa) (3.0.4)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from requests->pooch>=1.0->librosa) (1.25.11)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from packaging->pooch>=1.0->librosa) (2.4.7)\n",
      "Requirement already satisfied: pycparser in c:\\users\\swetha\\anaconda3\\lib\\site-packages (from cffi>=1.0->soundfile>=0.9.0->librosa) (2.20)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "(1080, 360)\n",
      "Features extracted: 180\n",
      "[[24  1  8  2  4  0  2 10]\n",
      " [ 0 41  2  0  0  0  2  0]\n",
      " [ 4  8 19  3  7  0  1  1]\n",
      " [ 1  5  3 21  6  0  4  4]\n",
      " [ 3  9  7  2 25  1  4  8]\n",
      " [ 0 20  3  0  1  2  2  3]\n",
      " [ 0 17  1  4  2  1 16  4]\n",
      " [ 0  7  3  0  4  0  2 26]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ReLu-Accuracy: 48.33%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       angry       0.75      0.47      0.58        51\n",
      "        calm       0.38      0.91      0.54        45\n",
      "     disgust       0.41      0.44      0.43        43\n",
      "     fearful       0.66      0.48      0.55        44\n",
      "       happy       0.51      0.42      0.46        59\n",
      "     neutral       0.50      0.06      0.11        31\n",
      "         sad       0.48      0.36      0.41        45\n",
      "   surprised       0.46      0.62      0.53        42\n",
      "\n",
      "    accuracy                           0.48       360\n",
      "   macro avg       0.52      0.47      0.45       360\n",
      "weighted avg       0.52      0.48      0.47       360\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\swetha\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[33  0  3  2  4  1  2  6]\n",
      " [ 0 36  2  0  2  2  1  2]\n",
      " [ 9  3 22  2  4  1  1  1]\n",
      " [ 0  0  4 30  5  0  4  1]\n",
      " [ 9  2  3  3 31  1  2  8]\n",
      " [ 0  8  2  0  1 17  0  3]\n",
      " [ 2  6  3  6  2  4 21  1]\n",
      " [ 1  2  2  0  4  1  2 30]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic-Accuracy: 61.11%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       angry       0.61      0.65      0.63        51\n",
      "        calm       0.63      0.80      0.71        45\n",
      "     disgust       0.54      0.51      0.52        43\n",
      "     fearful       0.70      0.68      0.69        44\n",
      "       happy       0.58      0.53      0.55        59\n",
      "     neutral       0.63      0.55      0.59        31\n",
      "         sad       0.64      0.47      0.54        45\n",
      "   surprised       0.58      0.71      0.64        42\n",
      "\n",
      "    accuracy                           0.61       360\n",
      "   macro avg       0.61      0.61      0.61       360\n",
      "weighted avg       0.61      0.61      0.61       360\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\swetha\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[34  0  6  2  2  0  2  5]\n",
      " [ 0 36  3  0  2  2  2  0]\n",
      " [ 9  3 19  4  5  0  1  2]\n",
      " [ 3  0  4 25  5  1  6  0]\n",
      " [ 6  2  5  6 29  0  4  7]\n",
      " [ 1 10  1  0  1 12  1  5]\n",
      " [ 2  6  3  5  4  3 21  1]\n",
      " [ 2  0  3  0  3  1  2 31]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tanh-Accuracy: 57.50%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       angry       0.60      0.67      0.63        51\n",
      "        calm       0.63      0.80      0.71        45\n",
      "     disgust       0.43      0.44      0.44        43\n",
      "     fearful       0.60      0.57      0.58        44\n",
      "       happy       0.57      0.49      0.53        59\n",
      "     neutral       0.63      0.39      0.48        31\n",
      "         sad       0.54      0.47      0.50        45\n",
      "   surprised       0.61      0.74      0.67        42\n",
      "\n",
      "    accuracy                           0.57       360\n",
      "   macro avg       0.58      0.57      0.57       360\n",
      "weighted avg       0.57      0.57      0.57       360\n",
      "\n",
      "[[30  2  3  2  6  0  4  4]\n",
      " [ 0 29  6  1  0  0  7  2]\n",
      " [ 5  6 17  2  6  2  1  4]\n",
      " [ 4  3  0 17  7  0  7  6]\n",
      " [ 5  7  2  8 23  0  5  9]\n",
      " [ 0  8  9  3  2  0  6  3]\n",
      " [ 2 12  2  9  4  0 11  5]\n",
      " [ 3  3  1  4  4  1  4 22]]\n",
      "Logistic-Accuracy: 41.39%\n",
      "[[30  2  3  2  6  0  4  4]\n",
      " [ 0 29  6  1  0  0  7  2]\n",
      " [ 5  6 17  2  6  2  1  4]\n",
      " [ 4  3  0 17  7  0  7  6]\n",
      " [ 5  7  2  8 23  0  5  9]\n",
      " [ 0  8  9  3  2  0  6  3]\n",
      " [ 2 12  2  9  4  0 11  5]\n",
      " [ 3  3  1  4  4  1  4 22]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\swetha\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run speech_recognition.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "emotion_genre={\"happy\":\"disco\",\"fearful\":\"classical\",\"disgust\":\"metal\",\"calm\":\"country\",\"neutral\":\"hiphop\",\"angry\":\"rock\",\"sad\":\"reggae\",\"surprised\":\"disco\"}\n",
    "\n",
    "emotion=speech_res()\n",
    "print(emotion)\n",
    "\n",
    "for i in emotion:\n",
    "    playmusic(emotion_genre[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}