MFCC_DTW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute the MFCCs of two sounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import librosa\n",
    "\n",
    "y1, sr1 = librosa.load('./sounds/10.wav')\n",
    "y2, sr2 = librosa.load('./sounds/78.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f195324ed68>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFb4HswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f195566ef28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import librosa.display # Must have\n",
    "%matplotlib inline\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "mfcc1 = librosa.feature.mfcc(y1, sr1)\n",
    "librosa.display.specshow(mfcc1)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "mfcc2 = librosa.feature.mfcc(y2, sr2)\n",
    "librosa.display.specshow(mfcc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 46)\n",
      "(20, 38)\n"
     ]
    }
   ],
   "source": [
    "# Number of MFCCs: 20 (Librosa default)\n",
    "print(mfcc1.shape)\n",
    "print(mfcc2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare them using DTW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized distance between the two sounds: 192.491546894\n"
     ]
    }
   ],
   "source": [
    "from dtw import dtw\n",
    "from numpy.linalg import norm\n",
    "\n",
    "dist, cost, acc_cost, path = dtw(mfcc1.T, mfcc2.T, dist=lambda x, y: norm(x - y, ord=1))\n",
    "print('Normalized distance between the two sounds:', dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46, 38)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 37.5)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASwAAAD8CAYAAADNNJnuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19531aa1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cost.T, origin='lower', cmap=plt.cm.gray, interpolation='nearest')\n",
    "plt.plot(path[0], path[1], 'w')\n",
    "plt.xlim((-0.5, cost.shape[0]-0.5))\n",
    "plt.ylim((-0.5, cost.shape[1]-0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}