Urban Sound Classification using NN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "import librosa\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import specgram\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "plt.rcParams['font.serif'] = 'Ubuntu'\n",
    "plt.rcParams['font.monospace'] = 'Ubuntu Mono'\n",
    "plt.rcParams['font.size'] = 12\n",
    "plt.rcParams['axes.labelsize'] = 11\n",
    "plt.rcParams['axes.labelweight'] = 'bold'\n",
    "plt.rcParams['axes.titlesize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 10\n",
    "plt.rcParams['ytick.labelsize'] = 10\n",
    "plt.rcParams['legend.fontsize'] = 11\n",
    "plt.rcParams['figure.titlesize'] = 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_sound_files(file_paths):\n",
    "    raw_sounds = []\n",
    "    for fp in file_paths:\n",
    "        X,sr = librosa.load(fp)\n",
    "        raw_sounds.append(X)\n",
    "    return raw_sounds\n",
    "\n",
    "def plot_waves(sound_names,raw_sounds):\n",
    "    i = 1\n",
    "    fig = plt.figure(figsize=(25,60), dpi = 900)\n",
    "    for n,f in zip(sound_names,raw_sounds):\n",
    "        plt.subplot(10,1,i)\n",
    "        librosa.display.waveplot(np.array(f),sr=22050)\n",
    "        plt.title(n.title())\n",
    "        i += 1\n",
    "    plt.suptitle('Figure 1: Waveplot',x=0.5, y=0.915,fontsize=18)\n",
    "    plt.show()\n",
    "    \n",
    "def plot_specgram(sound_names,raw_sounds):\n",
    "    i = 1\n",
    "    fig = plt.figure(figsize=(25,60), dpi = 900)\n",
    "    for n,f in zip(sound_names,raw_sounds):\n",
    "        plt.subplot(10,1,i)\n",
    "        specgram(np.array(f), Fs=22050)\n",
    "        plt.title(n.title())\n",
    "        i += 1\n",
    "    plt.suptitle('Figure 2: Spectrogram',x=0.5, y=0.915,fontsize=18)\n",
    "    plt.show()\n",
    "\n",
    "def plot_log_power_specgram(sound_names,raw_sounds):\n",
    "    i = 1\n",
    "    fig = plt.figure(figsize=(25,60), dpi = 900)\n",
    "    for n,f in zip(sound_names,raw_sounds):\n",
    "        plt.subplot(10,1,i)\n",
    "        D = librosa.logamplitude(np.abs(librosa.stft(f))**2, ref_power=np.max)\n",
    "        librosa.display.specshow(D,x_axis='time' ,y_axis='log')\n",
    "        plt.title(n.title())\n",
    "        i += 1\n",
    "    plt.suptitle('Figure 3: Log power spectrogram',x=0.5, y=0.915,fontsize=18)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sound_file_paths = [\"57320-0-0-7.wav\",\"24074-1-0-3.wav\",\"15564-2-0-1.wav\",\"31323-3-0-1.wav\",\"46669-4-0-35.wav\",\n",
    "                   \"89948-5-0-0.wav\",\"46656-6-0-0.wav\",\"103074-7-3-2.wav\",\"106905-8-0-0.wav\",\"108041-9-0-4.wav\"]\n",
    "sound_names = [\"air conditioner\",\"car horn\",\"children playing\",\"dog bark\",\"drilling\",\"engine idling\",\n",
    "               \"gun shot\",\"jackhammer\",\"siren\",\"street music\"]\n",
    "\n",
    "raw_sounds = load_sound_files(sound_file_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plot_waves(sound_names,raw_sounds)\n",
    "plot_specgram(sound_names,raw_sounds)\n",
    "plot_log_power_specgram(sound_names,raw_sounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def extract_feature(file_name):\n",
    "    X, sample_rate = librosa.load(file_name)\n",
    "    stft = np.abs(librosa.stft(X))\n",
    "    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T,axis=0)\n",
    "    chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)\n",
    "    mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)\n",
    "    contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)\n",
    "    tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0)\n",
    "    return mfccs,chroma,mel,contrast,tonnetz\n",
    "\n",
    "def parse_audio_files(parent_dir,sub_dirs,file_ext='*.wav'):\n",
    "    features, labels = np.empty((0,193)), np.empty(0)\n",
    "    for label, sub_dir in enumerate(sub_dirs):\n",
    "        for fn in glob.glob(os.path.join(parent_dir, sub_dir, file_ext)):\n",
    "            mfccs, chroma, mel, contrast,tonnetz = extract_feature(fn)\n",
    "            ext_features = np.hstack([mfccs,chroma,mel,contrast,tonnetz])\n",
    "            features = np.vstack([features,ext_features])\n",
    "            labels = np.append(labels, fn.split('/')[2].split('-')[1])\n",
    "    return np.array(features), np.array(labels, dtype = np.int)\n",
    "\n",
    "def one_hot_encode(labels):\n",
    "    n_labels = len(labels)\n",
    "    n_unique_labels = len(np.unique(labels))\n",
    "    one_hot_encode = np.zeros((n_labels,n_unique_labels))\n",
    "    one_hot_encode[np.arange(n_labels), labels] = 1\n",
    "    return one_hot_encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "parent_dir = 'Sound-Data'\n",
    "\n",
    "sub_dirs = ['fold1','fold2','fold3']\n",
    "features, labels = parse_audio_files(parent_dir,sub_dirs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels = one_hot_encode(labels)\n",
    "\n",
    "train_test_split = np.random.rand(len(features)) < 0.70\n",
    "train_x = features[train_test_split]\n",
    "train_y = labels[train_test_split]\n",
    "test_x = features[~train_test_split]\n",
    "test_y = labels[~train_test_split]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training Neural Network with TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from sklearn.metrics import precision_recall_fscore_support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "training_epochs = 5000\n",
    "n_dim = features.shape[1]\n",
    "n_classes = 10\n",
    "n_hidden_units_one = 280 \n",
    "n_hidden_units_two = 300\n",
    "sd = 1 / np.sqrt(n_dim)\n",
    "learning_rate = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = tf.placeholder(tf.float32,[None,n_dim])\n",
    "Y = tf.placeholder(tf.float32,[None,n_classes])\n",
    "\n",
    "W_1 = tf.Variable(tf.random_normal([n_dim,n_hidden_units_one], mean = 0, stddev=sd))\n",
    "b_1 = tf.Variable(tf.random_normal([n_hidden_units_one], mean = 0, stddev=sd))\n",
    "h_1 = tf.nn.tanh(tf.matmul(X,W_1) + b_1)\n",
    "\n",
    "\n",
    "W_2 = tf.Variable(tf.random_normal([n_hidden_units_one,n_hidden_units_two], mean = 0, stddev=sd))\n",
    "b_2 = tf.Variable(tf.random_normal([n_hidden_units_two], mean = 0, stddev=sd))\n",
    "h_2 = tf.nn.sigmoid(tf.matmul(h_1,W_2) + b_2)\n",
    "\n",
    "\n",
    "W = tf.Variable(tf.random_normal([n_hidden_units_two,n_classes], mean = 0, stddev=sd))\n",
    "b = tf.Variable(tf.random_normal([n_classes], mean = 0, stddev=sd))\n",
    "y_ = tf.nn.softmax(tf.matmul(h_2,W) + b)\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cost_function = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(y_), reduction_indices=[1])) \n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)\n",
    "\n",
    "correct_prediction = tf.equal(tf.argmax(y_,1), tf.argmax(Y,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cost_history = np.empty(shape=[1],dtype=float)\n",
    "y_true, y_pred = None, None\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    for epoch in range(training_epochs):            \n",
    "        _,cost = sess.run([optimizer,cost_function],feed_dict={X:train_x,Y:train_y})\n",
    "        cost_history = np.append(cost_history,cost)\n",
    "    \n",
    "    y_pred = sess.run(tf.argmax(y_,1),feed_dict={X: test_x})\n",
    "    y_true = sess.run(tf.argmax(test_y,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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bgpl2u8ocfpzchH/7XQoAAAgwQlsSmL/+Te6H2XKzZ/hdCgAACKi0ZJzEOadx48ZpyZIl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\ny8/DVSSVN2IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe313491c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "plt.plot(cost_history)\n",
    "plt.ylabel(\"Cost\")\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.axis([0,training_epochs,0,np.max(cost_history)])\n",
    "plt.show()\n",
    "\n",
    "p,r,f,s = precision_recall_fscore_support(y_true, y_pred, average='micro')\n",
    "print \"F-Score:\", round(f,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}