Cough Api calls.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FOR VISUALIZING THE FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13, 454)\n",
      "(12, 454)\n",
      "(1025, 454)\n",
      "(1, 454)\n",
      "(454, 1051)\n"
     ]
    }
   ],
   "source": [
    "import os \n",
    "import librosa\n",
    "import math\n",
    "import json\n",
    "import librosa.display\n",
    "\n",
    "path = \"/Users/Furqan Ali/Desktop/saylani_python/Coursera_learning/virufy_cleaned_file/cough_cleaned_touseef/Postive-con/pos-1.wav\"\n",
    "sr = 22050\n",
    "\n",
    "\n",
    "# Zero crossing rate\n",
    "y, sr = librosa.load(path, sr = sr)\n",
    "zc = librosa.feature.chroma_stft(y=y, sr=sr,n_chroma=12, n_fft=4096)\n",
    "\n",
    "mfcc = librosa.feature.mfcc(y,\n",
    "                                sr =sr,\n",
    "                                n_fft = 2048,\n",
    "                                n_mfcc = 13,\n",
    "                                hop_length = 512\n",
    "                                )\n",
    "                    \n",
    "chroma_cq = librosa.feature.chroma_cqt(y=y, sr=sr)\n",
    "\n",
    "pitches, magnitudes = librosa.piptrack(y=y, sr=sr)\n",
    "rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr, roll_percent=0.1)\n",
    "\n",
    "features = np.concatenate((mfcc, chroma_cq, pitches, rolloff), axis = 0)\n",
    "\n",
    "features = features.T\n",
    "# zc = librosa.feature.zero_crossing_rate(y)\n",
    "\n",
    "# print(spectral_entropy(zc, 100, method='welch', normalize=True))\n",
    "\n",
    "# librosa.display.waveplot(zc[0], sr)\n",
    "\n",
    "print(mfcc.shape)\n",
    "print(chroma_cq.shape)\n",
    "print(pitches.shape)\n",
    "print(rolloff.shape)\n",
    "print(features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for plotting features\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10, 4))\n",
    "librosa.display.specshow(features, y_axis='chroma', x_axis='time')\n",
    "plt.colorbar()\n",
    "plt.title('Chromagram')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing cough data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Processing Negative-con\n",
      "cough_cleaned_touseef\\Negative-con\\neg-1.wav, segment : 0\n",
      "cough_cleaned_touseef\\Negative-con\\neg-1.wav, segment : 1\n",
      "cough_cleaned_touseef\\Negative-con\\neg-1.wav, segment : 2\n",
      "cough_cleaned_touseef\\Negative-con\\neg-11.wav, segment : 0\n",
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      "cough_cleaned_touseef\\Negative-con\\neg-16.wav, segment : 0\n",
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      "cough_cleaned_touseef\\Negative-con\\neg-2.wav, segment : 0\n",
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      "cough_cleaned_touseef\\Negative-con\\neg-20.wav, segment : 0\n",
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      "cough_cleaned_touseef\\Negative-con\\neg-21.wav, segment : 0\n",
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      "cough_cleaned_touseef\\Negative-con\\neg-3.wav, segment : 0\n",
      "cough_cleaned_touseef\\Negative-con\\neg-3.wav, segment : 1\n",
      "cough_cleaned_touseef\\Negative-con\\neg-3.wav, segment : 2\n",
      "cough_cleaned_touseef\\Negative-con\\neg-5.wav, segment : 0\n",
      "cough_cleaned_touseef\\Negative-con\\neg-5.wav, segment : 1\n",
      "cough_cleaned_touseef\\Negative-con\\neg-5.wav, segment : 2\n",
      "cough_cleaned_touseef\\Negative-con\\neg-6.wav, segment : 0\n",
      "cough_cleaned_touseef\\Negative-con\\neg-6.wav, segment : 1\n",
      "cough_cleaned_touseef\\Negative-con\\neg-6.wav, segment : 2\n",
      "cough_cleaned_touseef\\Negative-con\\neg-7.wav, segment : 0\n",
      "cough_cleaned_touseef\\Negative-con\\neg-7.wav, segment : 1\n",
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      "cough_cleaned_touseef\\Negative-con\\neg-8.wav, segment : 0\n",
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      "cough_cleaned_touseef\\Negative-con\\neg-9.wav, segment : 2\n",
      "\n",
      " Processing Postive-con\n",
      "cough_cleaned_touseef\\Postive-con\\pos-1.wav, segment : 0\n",
      "cough_cleaned_touseef\\Postive-con\\pos-1.wav, segment : 1\n",
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      "cough_cleaned_touseef\\Postive-con\\pos-12.wav, segment : 0\n",
      "cough_cleaned_touseef\\Postive-con\\pos-12.wav, segment : 1\n",
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      "cough_cleaned_touseef\\Postive-con\\pos-13.wav, segment : 0\n",
      "cough_cleaned_touseef\\Postive-con\\pos-13.wav, segment : 1\n",
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      "cough_cleaned_touseef\\Postive-con\\pos-16.wav, segment : 0\n",
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      "cough_cleaned_touseef\\Postive-con\\pos-5.wav, segment : 0\n",
      "cough_cleaned_touseef\\Postive-con\\pos-5.wav, segment : 1\n",
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      "cough_cleaned_touseef\\Postive-con\\pos-6.wav, segment : 0\n",
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      "cough_cleaned_touseef\\Postive-con\\pos-6.wav, segment : 2\n",
      "cough_cleaned_touseef\\Postive-con\\pos-7.wav, segment : 0\n",
      "cough_cleaned_touseef\\Postive-con\\pos-7.wav, segment : 1\n",
      "cough_cleaned_touseef\\Postive-con\\pos-7.wav, segment : 2\n",
      "cough_cleaned_touseef\\Postive-con\\pos-8.wav, segment : 0\n",
      "cough_cleaned_touseef\\Postive-con\\pos-8.wav, segment : 1\n",
      "cough_cleaned_touseef\\Postive-con\\pos-8.wav, segment : 2\n",
      "cough_cleaned_touseef\\Postive-con\\pos-9.wav, segment : 0\n",
      "cough_cleaned_touseef\\Postive-con\\pos-9.wav, segment : 1\n",
      "cough_cleaned_touseef\\Postive-con\\pos-9.wav, segment : 2\n"
     ]
    }
   ],
   "source": [
    "# preprocessing testing\n",
    "\n",
    "import cough_preprocess\n",
    "\n",
    "DATASET_PATH = \"cough_cleaned_touseef\"\n",
    "JSON_PATH = \"testing.json\"\n",
    "DURATION = 8  # measured in seconds.\n",
    "num_segments = 3\n",
    "\n",
    "cough_preprocess.save_features(DATASET_PATH, JSON_PATH, num_segments = num_segments, duration = DURATION )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training cough model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training the cough model\n",
    "\n",
    "import cough_model as cm\n",
    "\n",
    "    \n",
    "# # Calling random forest class here:\n",
    "DATASET_PATH = \"fused_features.json\"\n",
    "save_model_in = \"testing.sav\"\n",
    "test_size = 30\n",
    "ran_state = 10\n",
    "rf_maxdepth = 10\n",
    "rf_ran_state = 170\n",
    "rf_estimators = 150\n",
    "\n",
    "ff = cm.RF_Model(DATASET_PATH, save_model_in, test_size, ran_state, rf_maxdepth, rf_ran_state, rf_estimators)\n",
    "mod = ff.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Doing Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results are :  [[0.56666667 0.43333333]\n",
      " [0.46       0.54      ]\n",
      " [0.56       0.44      ]]\n",
      "class neg:  [0.5666666666666667, 0.56]\n",
      "class pos:  [0.54]\n",
      "Covid positive rate:  0\n"
     ]
    }
   ],
   "source": [
    "# doing prediction\n",
    "\n",
    "import numpy as np\n",
    "import cough as CP\n",
    "\n",
    "file_path = \"./recent_toufeeq_cough_data/Negative-con/neg-61.wav\"\n",
    "saved_model = 'fused_results.sav'\n",
    "\n",
    "gg = CP.predict(file_path, saved_model)\n",
    "print(\"Covid positive rate: \", gg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note \n",
    "\n",
    "# why dont we just make many segments of our audio sample and then classiy all those segments al together and then take a mean\n",
    "# of all those samples and give 1 output so lets say if we have noise in the cough so normally when we have multiple segments \n",
    "# so some of segents will be containing noise and some of them will be containng meaningfull iformation and so when u do \n",
    "# classification on all those segments so most prbbaly the part which is conating cough will always predict right and the one \n",
    "# containng nouse will not get predicted well. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extracting features using Mel spectograms technique. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1450, 64)\n"
     ]
    }
   ],
   "source": [
    "from mel_features import log_mel_spectrogram as lms\n",
    "\n",
    "\n",
    "path = \"/Users/Furqan Ali/Desktop/saylani_python/Coursera_learning/virufy_cleaned_file/cough_cleaned_touseef/Postive-con/pos-1.wav\"\n",
    "\n",
    "# Zero crossing rate\n",
    "y, sr = librosa.load(path, sr = 22050)\n",
    "\n",
    "features = lms(y, audio_sample_rate=16000,\n",
    "                log_offset=0.01, window_length_secs=0.025, hop_length_secs= 0.010, num_mel_bins=64, \n",
    "                lower_edge_hertz=125, upper_edge_hertz= 7500\n",
    "               )\n",
    "\n",
    "print(features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for plotting features\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10, 4))\n",
    "librosa.display.specshow(features, y_axis='mel', x_axis='time')\n",
    "plt.colorbar()\n",
    "plt.title('mel features')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment : 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Processing Negative\n",
      "Re-Cough\\Negative\\neg-0423-118-cough-m-t-56.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-0423-118-cough-m-t-56.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-0423-118-cough-m-t-56.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-0423-119-cough-f-t-25.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-0423-119-cough-f-t-25.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-0423-119-cough-f-t-25.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-0423-120-cough-m-t-30.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-0423-120-cough-m-t-30.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-0423-120-cough-m-t-30.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-0423-122-cough-m-t-36.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-0423-122-cough-m-t-36.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-0423-122-cough-m-t-36.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-0423-123-cough-m-t-48.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-0423-123-cough-m-t-48.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-0423-123-cough-m-t-48.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-1-c.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-1-c.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-1-c.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-20-c.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-20-c.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-20-c.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-24-c.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-24-c.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-24-c.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-3-c.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-3-c.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-3-c.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-30-c.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-30-c.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-30-c.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-51.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-51.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-51.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-52.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-52.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-52.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-53.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-53.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-53.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-54.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-54.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-54.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-55.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-55.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-55.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-56.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-56.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-56.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-57.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-57.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-57.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-58.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-58.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-58.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-59.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-59.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-59.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-6-c.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-6-c.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-6-c.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-60.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-60.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-60.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-61.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-61.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-61.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-62.wav, segment : 0\n",
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      "Re-Cough\\Negative\\neg-62.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-63.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-63.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-63.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-64.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-64.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-64.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-65.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-65.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-65.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-66.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-66.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-66.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-67.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-67.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-67.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-68.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-68.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-68.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-69.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-69.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-69.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-7-c.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-7-c.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-7-c.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-70.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-70.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-70.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-71.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-71.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-71.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-72.wav, segment : 0\n",
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      "Re-Cough\\Negative\\neg-72.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-73.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-73.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-73.wav, segment : 2\n",
      "Re-Cough\\Negative\\neg-74.wav, segment : 0\n",
      "Re-Cough\\Negative\\neg-74.wav, segment : 1\n",
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      "Re-Cough\\Negative\\neg-75.wav, segment : 0\n",
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      "Re-Cough\\Negative\\neg-76.wav, segment : 1\n",
      "Re-Cough\\Negative\\neg-76.wav, segment : 2\n",
      "\n",
      " Processing Positive\n",
      "Re-Cough\\Positive\\pos-026-124-cough-m-t-38.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-026-124-cough-m-t-38.wav, segment : 1\n",
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      "Re-Cough\\Positive\\pos-026-125-cough-m-t-30.wav, segment : 0\n",
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      "Re-Cough\\Positive\\pos-026-126-cough-m-t-46.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-026-126-cough-m-t-46.wav, segment : 1\n",
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      "Re-Cough\\Positive\\pos-026-127-cough-m-t-34.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-026-127-cough-m-t-34.wav, segment : 1\n",
      "Re-Cough\\Positive\\pos-026-127-cough-m-t-34.wav, segment : 2\n",
      "Re-Cough\\Positive\\pos-026-128-cough-m-t-37.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-026-128-cough-m-t-37.wav, segment : 1\n",
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      "Re-Cough\\Positive\\pos-026-129-cough-f-t-26.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-026-129-cough-f-t-26.wav, segment : 1\n",
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      "Re-Cough\\Positive\\pos-0423-121-cough-m-t-36.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-0423-121-cough-m-t-36.wav, segment : 1\n",
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      "Re-Cough\\Positive\\pos-1.wav, segment : 0\n",
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      "Re-Cough\\Positive\\pos-15.wav, segment : 0\n",
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      "Re-Cough\\Positive\\pos-15.wav, segment : 2\n",
      "Re-Cough\\Positive\\pos-16.wav, segment : 0\n",
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      "Re-Cough\\Positive\\pos-16.wav, segment : 2\n",
      "Re-Cough\\Positive\\pos-17.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-17.wav, segment : 1\n",
      "Re-Cough\\Positive\\pos-17.wav, segment : 2\n",
      "Re-Cough\\Positive\\pos-18.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-18.wav, segment : 1\n",
      "Re-Cough\\Positive\\pos-18.wav, segment : 2\n",
      "Re-Cough\\Positive\\pos-19.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-19.wav, segment : 1\n",
      "Re-Cough\\Positive\\pos-19.wav, segment : 2\n",
      "Re-Cough\\Positive\\pos-20.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-20.wav, segment : 1\n",
      "Re-Cough\\Positive\\pos-20.wav, segment : 2\n",
      "Re-Cough\\Positive\\pos-21.wav, segment : 0\n",
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      "Re-Cough\\Positive\\pos-22.wav, segment : 0\n",
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      "Re-Cough\\Positive\\pos-23.wav, segment : 0\n",
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      "Re-Cough\\Positive\\pos-24.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-24.wav, segment : 1\n",
      "Re-Cough\\Positive\\pos-24.wav, segment : 2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-Cough\\Positive\\pos-25.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-25.wav, segment : 1\n",
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      "Re-Cough\\Positive\\pos-5.wav, segment : 2\n",
      "Re-Cough\\Positive\\pos-6.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-6.wav, segment : 1\n",
      "Re-Cough\\Positive\\pos-6.wav, segment : 2\n",
      "Re-Cough\\Positive\\pos-7.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-7.wav, segment : 1\n",
      "Re-Cough\\Positive\\pos-7.wav, segment : 2\n",
      "Re-Cough\\Positive\\pos-8.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-8.wav, segment : 1\n",
      "Re-Cough\\Positive\\pos-8.wav, segment : 2\n",
      "Re-Cough\\Positive\\pos-9.wav, segment : 0\n",
      "Re-Cough\\Positive\\pos-9.wav, segment : 1\n",
      "Re-Cough\\Positive\\pos-9.wav, segment : 2\n"
     ]
    }
   ],
   "source": [
    "import os \n",
    "import librosa\n",
    "import numpy as np\n",
    "import math\n",
    "import json\n",
    "from mel_features import log_mel_spectrogram as lms\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "DATASET_PATH = \"Re-Cough\"\n",
    "JSON_PATH = \"testing.json\"\n",
    "# SAMPLE_RATE = 22050\n",
    "DURATION = 10  # measured in seconds.\n",
    "# SAMPLES_PER_TRACK = SAMPLE_RATE * DURATION\n",
    "\n",
    "\n",
    "def save_features(dataset_path, json_path, n_mfcc = 13, n_fft = 2048, hop_length= 512, num_segments = 5, duration = 8):\n",
    "   \n",
    "    # dictionary to store data\n",
    "    data = {\n",
    "        \"mapping\": [],\n",
    "        \"features\": [],\n",
    "        \"labels\": []\n",
    "    }\n",
    "    \n",
    "    SAMPLE_RATE = 16000\n",
    "    SAMPLES_PER_TRACK = SAMPLE_RATE * duration\n",
    "    \n",
    "    num_samples_per_segment =  int(SAMPLES_PER_TRACK / num_segments)\n",
    "    \n",
    "    expected_no_of_mfcc_vectors_per_segment = math.ceil(num_samples_per_segment / hop_length) \n",
    "    \n",
    "    for i, (dirpath, dirname, filenames) in enumerate(os.walk(dataset_path)):\n",
    "        \n",
    "        # Ensure that we r not at the root level\n",
    "        if dirpath is not dataset_path:\n",
    "            dirpath_components = dirpath.split(\"\\\\\")  # \"genre/blues\" => [\"genre\", \"blues\"]\n",
    "            semantic_label = dirpath_components[-1]\n",
    "            data[\"mapping\"].append(semantic_label)\n",
    "            \n",
    "            print(\"\\n Processing {}\".format(semantic_label))\n",
    "            \n",
    "            # Process files for specific genre. \n",
    "            for f in filenames:\n",
    "                # loading audio file\n",
    "                file_path = os.path.join(dirpath, f)\n",
    "                signal, sr = librosa.load(file_path, sr = SAMPLE_RATE)\n",
    "                \n",
    "                # divide signals into segments,process segments, extract mfcc and at last store mfcc vectors.\n",
    "                for s in range(num_segments):\n",
    "                    start_sample = num_samples_per_segment * s  # if s= 0 -> then start_sample = 0 \n",
    "                    finish_sample = start_sample + num_samples_per_segment \n",
    "                    \n",
    "                    # feature\n",
    "                    features = lms(signal[start_sample: finish_sample], audio_sample_rate=16000,\n",
    "                                    log_offset=0.01, window_length_secs=0.025, hop_length_secs= 0.010, num_mel_bins=64, \n",
    "                                    lower_edge_hertz=125, upper_edge_hertz= 7500\n",
    "                                   )\n",
    "                    \n",
    "                    data[\"features\"].append(features.tolist()) # we can not append mfcc directly blc its a numpy array so we have\n",
    "                    # to first convert it into list.\n",
    "\n",
    "                    data[\"labels\"].append(i-1)\n",
    "                    # i mean in each iteration we are in different genre folder. why r we doing i -1 that's blc in first \n",
    "                    # iteration we were not inside the dir containing all the genres folder. \n",
    "\n",
    "                    print(\"{}, segment : {}\".format(file_path, s))\n",
    "\n",
    "    # final step: saving everything as a json file            \n",
    "    with open(json_path, \"w\") as fp:\n",
    "        json.dump(data, fp, indent = 4)  # indent mean spaces while writing. fp mean file_path  \n",
    "        \n",
    "        \n",
    "if __name__ == \"__main__\":\n",
    "    save_features(DATASET_PATH, JSON_PATH, num_segments = 3, duration = DURATION)  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(261, 331, 64)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import json\n",
    "\n",
    "DATASET_PATH = \"testing.json\"\n",
    "\n",
    "def load_data(dataset_path):\n",
    "    with open(dataset_path, \"r\") as fp:\n",
    "        data = json.load(fp)\n",
    "        \n",
    "    # converting lists into numpy arrays\n",
    "    X = np.array(data[\"features\"])\n",
    "    y = np.array(data[\"labels\"])\n",
    "    \n",
    "    return X, y\n",
    "\n",
    "X, y = load_data(DATASET_PATH)\n",
    "\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import librosa, librosa.display\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "librosa.display.specshow(X[1], y_axis='mel', x_axis='time')\n",
    "plt.colorbar()\n",
    "plt.title('mel features')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e312d89a08>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# if u have 3 color channels image, u need to uncomment below line. it will take mean if color channel to make it single channel image\n",
    "# g = np.mean(X[0], -1)  # convert RGB to gray scale\n",
    "# img = plt.imshow(X[0])\n",
    "fig = plt.figure(figsize=(20, 30))\n",
    "# for i in range(1, 5):\n",
    "#   ax = fig.add_subplot(2, i, 1)\n",
    "#   ax.imshow(X[i], cmap=\"gray\")\n",
    "\n",
    "ax = fig.add_subplot(6, 1, 1)\n",
    "ax.imshow(X[1], cmap = \"gray\")\n",
    "\n",
    "ax = fig.add_subplot(6, 2, 1)\n",
    "ax.imshow(X[2], cmap = \"gray\")\n",
    "\n",
    "ax = fig.add_subplot(6, 3, 1)\n",
    "ax.imshow(X[3], cmap = \"gray\")\n",
    "\n",
    "# img.set_cmap(\"gray\")\n",
    "# plt.axis(\"off\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Doing training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hyper parameters for SVC\n",
    "kernel = [\"linear\",\"poly\",\"rbf\",\"sigmoid\"]\n",
    "degree = [2,3,4]\n",
    "# gammas = [0.001, 0.01, 0.1, 1, 10, 100]\n",
    "gammas = [0.001, 0.01, 0.1, 1, 10]\n",
    "# making C list  \n",
    "C=[1, 10, 100, 200, 300, 400]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(138, 21184) (70, 21184)\n",
      "Training shape after flattening:  (208, 21184)\n",
      "Training started ....\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "import librosa\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn import metrics\n",
    "\n",
    "train, test, pred_train, pred_test = train_test_split(X, y, test_size = 0.20, random_state=20)\n",
    "\n",
    "train = train.reshape(train.shape[0], (train.shape[1] * train.shape[2]))\n",
    "test = test.reshape(test.shape[0], (test.shape[1] * test.shape[2]))\n",
    "\n",
    "scores_svm = []\n",
    "\n",
    "# prepare cross validation\n",
    "kfold = KFold(3, True, 1)   # (folds ,shuffle, random_number)\n",
    "\n",
    "for train_index, test_index in kfold.split(train):\n",
    "    X_train, X_test = train[train_index], train[test_index]\n",
    "    y_train, y_test = pred_train[train_index], pred_train[test_index]\n",
    "    print(X_train.shape, X_test.shape)\n",
    "\n",
    "    print(\"Training shape after flattening: \", train.shape)\n",
    "    print(\"Training started ....\")\n",
    "    clf = svm.SVC(C=1, gamma= 10, degree = 4, kernel='poly', random_state=0)\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "    acc = clf.score(X_test, y_test)\n",
    "    print(\"acc=%0.3f\" % acc)\n",
    "    scores_svm.append(acc)\n",
    "\n",
    "print(\"Mean score is: \", np.mean(scores_svm))\n",
    "\n",
    "  # y_pred = clf.predict(X_test)\n",
    "  # print(metrics.classification_report(y_test, y_pred))  \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# accuracy_history = []\n",
    "# for i in kernel:\n",
    "#     for j in degree:\n",
    "#         for k in gammas:\n",
    "#             for l in C:\n",
    "#                 clf = svm.SVC(kernel = i,C=l, degree = j, gamma=k,random_state=0)\n",
    "#                 clf.fit(train,y_train)\n",
    "#                 pred = clf.predict(test)\n",
    "#                 accuracy = clf.score(test, y_test)\n",
    "#                 print(\"Accuracy on test dataset with\",i,\"kernel\",j,\"degree\",k,\"gamma\",l,\"C: \", accuracy)\n",
    "#                 accuracy_history.append(accuracy)\n",
    "\n",
    "# clf = svm.SVC(C=100, gamma= 100, degree = 4, kernel='poly', random_state=5)\n",
    "# clf.fit(X_train, y_train)\n",
    "\n",
    "# acc = clf.score(X_test, y_test)\n",
    "# print(\"acc=%0.3f\" % acc)\n",
    "\n",
    "# from sklearn import metrics\n",
    "# y_pred = clf.predict(X_test)\n",
    "# print(metrics.classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow_env",
   "language": "python",
   "name": "tensorflow_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
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