{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from pathlib import Path\n",
    "import numpy, scipy, matplotlib.pyplot as plt, sklearn, urllib, IPython.display as ipd\n",
    "import librosa, librosa.display\n",
    "import stanford_mir; stanford_mir.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[← Back to Index](index.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Feature Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Somehow, we must extract the characteristics of our audio signal that are most relevant to the problem we are trying to solve. For example, if we want to classify instruments by timbre, we will want features that distinguish sounds by their timbre and not their pitch. If we want to perform pitch detection, we want features that distinguish pitch and not timbre.\n",
    "\n",
    "This process is known as feature extraction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's begin with twenty audio files: ten kick drum samples, and ten snare drum samples. Each audio file contains one drum hit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read and store each signal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "kick_signals = [\n",
    "    librosa.load(p)[0] for p in Path().glob('audio/drum_samples/train/kick_*.mp3')\n",
    "]\n",
    "snare_signals = [\n",
    "    librosa.load(p)[0] for p in Path().glob('audio/drum_samples/train/snare_*.mp3')\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(kick_signals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(snare_signals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the kick drum signals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "for i, x in enumerate(kick_signals):\n",
    "    plt.subplot(2, 5, i+1)\n",
    "    librosa.display.waveplot(x[:10000])\n",
    "    plt.ylim(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the snare drum signals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "for i, x in enumerate(snare_signals):\n",
    "    plt.subplot(2, 5, i+1)\n",
    "    librosa.display.waveplot(x[:10000])\n",
    "    plt.ylim(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Constructing a Feature Vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A *feature vector* is simply a collection of features. Here is a simple function that constructs a two-dimensional feature vector from a signal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_features(signal):\n",
    "    return [\n",
    "        librosa.feature.zero_crossing_rate(signal)[0, 0],\n",
    "        librosa.feature.spectral_centroid(signal)[0, 0],\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to aggregate all of the feature vectors among signals in a collection, we can use a list comprehension as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "kick_features = numpy.array([extract_features(x) for x in kick_signals])\n",
    "snare_features = numpy.array([extract_features(x) for x in snare_signals])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the differences in features by plotting separate histograms for each of the classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 5))\n",
    "plt.hist(kick_features[:,0], color='b', range=(0, 0.2), alpha=0.5, bins=20)\n",
    "plt.hist(snare_features[:,0], color='r', range=(0, 0.2), alpha=0.5, bins=20)\n",
    "plt.legend(('kicks', 'snares'))\n",
    "plt.xlabel('Zero Crossing Rate')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Count')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 5))\n",
    "plt.hist(kick_features[:,1], color='b', range=(0, 4000), bins=30, alpha=0.6)\n",
    "plt.hist(snare_features[:,1], color='r', range=(0, 4000), bins=30, alpha=0.6)\n",
    "plt.legend(('kicks', 'snares'))\n",
    "plt.xlabel('Spectral Centroid (frequency bin)')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The features that we used in the previous example included zero crossing rate and spectral centroid. These two features are expressed using different units. This discrepancy can pose problems when performing classification later. Therefore, we will normalize each feature vector to a common range and store the normalization parameters for later use.  \n",
    "\n",
    "Many techniques exist for scaling your features. For now, we'll use [`sklearn.preprocessing.MinMaxScaler`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html).  `MinMaxScaler` returns an array of scaled values such that each feature dimension is in the range -1 to 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's concatenate all of our feature vectors into one *feature table*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 2)\n"
     ]
    }
   ],
   "source": [
    "feature_table = numpy.vstack((kick_features, snare_features))\n",
    "print(feature_table.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale each feature dimension to be in the range -1 to 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1. -1.]\n",
      "[1. 1.]\n"
     ]
    }
   ],
   "source": [
    "scaler = sklearn.preprocessing.MinMaxScaler(feature_range=(-1, 1))\n",
    "training_features = scaler.fit_transform(feature_table)\n",
    "print(training_features.min(axis=0))\n",
    "print(training_features.max(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the scaled features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Spectral Centroid')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(training_features[:10,0], training_features[:10,1], c='b')\n",
    "plt.scatter(training_features[10:,0], training_features[10:,1], c='r')\n",
    "plt.xlabel('Zero Crossing Rate')\n",
    "plt.ylabel('Spectral Centroid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[&larr; Back to Index](index.html)"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}