{
 "metadata": {
  "name": "",
  "signature": "sha256:9733e855fe9a1da482edf975c18e81dce647d9d28b2f7896a11c72c436817884"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Feature Extraction"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load, plot, and play `simpleLoop.wav`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from essentia.standard import MonoLoader\n",
      "x = MonoLoader(filename='simpleLoop.wav')()\n",
      "print x.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(132300,)\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = arange(len(x))/44100.0\n",
      "plot(t, x)\n",
      "xlabel('Time (seconds)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "<matplotlib.text.Text at 0x3f49450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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P7hIFLuivyezZs7F27VoMGzYM69atw+zZswEAhw8fRkFBAQCgtrYW2dnZSE9P\nR1ZWFm6++WaMGzeu3WU6ocXvJEOG6HddhlYtfjZGyI6CPrnbv39/fPTRR23eHzJkCEpKSgAAV1xx\nBXbu3BnwMn19afnFM7/2ds41NfqVp9cFXER2YKoDY18hz+CnQLCPnyhwpvqaOCH47diC1HudjL47\nJ5HVmepePd4h/+abQNeuxtSFrIXBTxQ4Uwf/1KnG1IM6xwxHMRERrada2LtXu2UT6clU7SO7deuQ\nfiIjgQEDtH30onzMI5HVMfhJNTP08YeHA999p289iKyKwa+jqCjgueeMrgUROZ2pgt/uJ+auu050\nR9iNGfr4iShwpopau7f4iYjMgMFPqtm5xW/ndSPnMlXwEwWCYUykDoOfVDPDqB4iChyDX0fsyiIi\nM2Dwk2psgRNZC4NfR2zxWw93amRHpgp+BqM1OSEc777b6BoQhY6pgt/uZs40ugb2YMSO5tVX9S+T\nSCsMfp2MHQtMmGB0LbThhBY/kZ0w+HXCbixr4k6N7IjBT6pxHD+RtTD4STUGMZG1MPh1Mny40TUg\nIhJM9ehFu7pwQdtHAhqNXT1E1mKq4LfrCdDu3Y2uAQWL247siF09pJqdW+A9eth7/ciZGPxERA7D\n4CfVPvjA6BoQUWcw+Em1gweNrgERdYapgj/cVKeayazY506kjqmCPyzM6BpQMG65Rd/yGPxE6jD4\nSbXXXgM++sjoWhBRoBj8pNqll4q7jxKRNTD4iYgchsFPROQwDH6yHJ7cJVIn6OB/7733MGLECISF\nhaGioqLd+UpLS5GUlITExEQsWLDA7zIZ/BQIBj+ROkEHf0pKClasWIHRo0e3O09TUxNmzZqF0tJS\n7N69G4sXL8aePXvanT8+PtjakJM0NxtdAyJrC/qSqaSkpA7nKS8vR0JCAuJ/TPTJkydj5cqVSE5O\nbjMvW3EUqKYmo2tAZG2aXitbU1ODuLi4lp9jY2Oxbds2n/POmTOn5XVOTg5ycnK0rBpZGFv85FRl\nZWUoKytTvRy/wZ+bm4va2to27z/11FOYMGFChwt3deIG+57BT+TP008Du3cbXQsi/Xk3iufOnRvU\ncvwG/9q1a4NaqBQTE4OqqqqWn6uqqhAbG6tqmUSjR4t/RBSckAznVNrpoB81ahT279+PyspK1NfX\nY+nSpSgsLAxFkUREFKSgg3/FihWIi4vD1q1bUVBQgPz8fADA4cOHUVBQAAAIDw/HwoULkZeXh+HD\nh+OXv/yfKJ6qAAALdElEQVSlzxO7RESkH5fSXnNdz0q4XO0eNRARkW/BZqeprtwlIiLtMfiJiByG\nwU9E5DAMfiIih2HwExE5DIOfiMhhGPxERA7D4CcichgGPxGRwzD4iYgchsFPROQwDH4iIodh8BMR\nOQyDn4jIYRj8REQOw+AnInIYBj8RkcMw+ImIHIbBT0TkMAx+IiKHYfATETkMg5+IyGEY/EREDsPg\nJyJyGAY/EZHDMPiJiByGwU9E5DAMfiIih2HwExE5DIOfiMhhGPxERA7D4CcichgGPxGRwzD4iYgc\nhsFPROQwDH4dlJWVGV0Fzdh53QCun9XZff2CFXTwv/feexgxYgTCwsJQUVHR7nzx8fFITU1FRkYG\nrrnmmmCLszQ7f/jsvG4A18/q7L5+wQoP9j+mpKRgxYoVKCoq8jufy+VCWVkZ+vfvH2xRREQUQkEH\nf1JSUsDzKooSbDFERBRiLkVlKo8ZMwbPPvssMjMzff7+iiuuQFRUFMLCwlBUVISZM2e2rYTLpaYK\nRESOFUyE+23x5+bmora2ts37Tz31FCZMmBBQAZs3b8bgwYNx7Ngx5ObmIikpCdnZ2a3m4REBEZF+\n/Ab/2rVrVRcwePBgAMCAAQMwadIklJeXtwl+IiLST0iGc7bXYq+rq8PZs2cBAOfPn8eaNWuQkpIS\niiKJiChIQQf/ihUrEBcXh61bt6KgoAD5+fkAgMOHD6OgoAAAUFtbi+zsbKSnpyMrKws333wzxo0b\nF5qaExFRcBQdrV69WrnyyiuVhIQEZf78+T7nuf/++5WEhAQlNTVVqaio0LN6qnW0fuvXr1f69Omj\npKenK+np6cqTTz5pQC2DM336dGXgwIHKyJEj253Hytuuo/Wz8rY7dOiQkpOTowwfPlwZMWKE8vzz\nz/ucz6rbL5D1s/L2u3DhgnLNNdcoaWlpSnJysjJ79myf83Vm++kW/I2NjcpPfvIT5eDBg0p9fb2S\nlpam7N69u9U8JSUlSn5+vqIoirJ161YlKytLr+qpFsj6rV+/XpkwYYJBNVRnw4YNSkVFRbvBaOVt\npygdr5+Vt92RI0eUHTt2KIqiKGfPnlWGDRtmq+9eIOtn5e2nKIpy/vx5RVEUpaGhQcnKylI2btzY\n6ved3X663bKhvLwcCQkJiI+PR0REBCZPnoyVK1e2mqe4uBjTpk0DAGRlZeHUqVM4evSoXlVUJZD1\nA6w7gik7Oxv9+vVr9/dW3nZAx+sHWHfbDRo0COnp6QCAXr16ITk5GYcPH241j5W3XyDrB1h3+wFA\nZGQkAKC+vh5NTU1tLojt7PbTLfhramoQFxfX8nNsbCxqamo6nKe6ulqvKqoSyPq5XC5s2bIFaWlp\nGD9+PHbv3q13NTVj5W0XCLtsu8rKSuzYsQNZWVmt3rfL9mtv/ay+/Zqbm5Geno7o6GiMGTMGw4cP\nb/X7zm6/oK/c7axAL9Ly3itb5eKuQOqZmZmJqqoqREZGYvXq1Zg4cSL27dunQ+30YdVtFwg7bLtz\n587h5z//OZ5//nn06tWrze+tvv38rZ/Vt1+XLl2wc+dOnD59Gnl5eSgrK0NOTk6reTqz/XRr8cfE\nxKCqqqrl56qqKsTGxvqdp7q6GjExMXpVUZVA1q93794th2z5+floaGjAiRMndK2nVqy87QJh9W3X\n0NCAW2+9FVOmTMHEiRPb/N7q26+j9bP69pOioqJQUFCA7du3t3q/s9tPt+AfNWoU9u/fj8rKStTX\n12Pp0qUoLCxsNU9hYSHefPNNAMDWrVvRt29fREdH61VFVQJZv6NHj7bslcvLy6Eoim1uXmflbRcI\nK287RVEwY8YMDB8+HL/97W99zmPl7RfI+ll5+x0/fhynTp0CAFy4cAFr165FRkZGq3k6u/106+oJ\nDw/HwoULkZeXh6amJsyYMQPJycl4+eWXAQBFRUUYP348Vq1ahYSEBPTs2ROLFi3Sq3qqBbJ+y5Yt\nw0svvYTw8HBERkZiyZIlBtc6cLfffjs++eQTHD9+HHFxcZg7dy4aGhoAWH/bAR2vn5W33ebNm/HW\nW2+13B4dELddOXToEADrb79A1s/K2+/IkSOYNm0ampub0dzcjKlTp2Ls2LGqslP1TdqIiMha+AQu\nIiKHYfATETkMg5+IyGEY/EREDsPgJ9P4/vvvkZGRgYyMDAwePBixsbHIyMhA7969MWvWLE3KXLhw\nIV5//XVNlh2M+Ph4v+PLb7vtNhw8eFDHGpEdcVQPmdLcuXPRu3dvPPzww5qVoSgKMjMz8c9//hPh\n4bqNbPZr6NCh+Oyzz9odY7527Vp8+OGHeOGFF3SuGdkJW/xkWrJNUlZW1vKozzlz5mDatGkYPXo0\n4uPjsXz5cjzyyCNITU1Ffn4+GhsbAQCfffYZcnJyMGrUKNx0000+HyG6efNmJCUltYT+Cy+8gBEj\nRiAtLQ233347APEAobvuugtZWVnIzMxEcXExAKCpqQmPPPIIUlJSkJaWhoULFwIAPv74Y2RmZiI1\nNRUzZsxAfX09ANGSnzNnDq666iqkpqZi7969AMRRzrhx4zBy5EjMnDmzZZ3Pnz+PgoICpKenIyUl\nBe+++y4AICcnB6tWrQr9H5schcFPlnPw4EGsX78excXFmDJlCnJzc7Fr1y706NEDJSUlaGhowP33\n34/3338f27dvx/Tp0/H73/++zXI2bdqEUaNGtfy8YMEC7Ny5E59//nnLxTHz5s3D2LFjsW3bNqxb\ntw6PPvoo6urq8Morr+DQoUP4/PPP8fnnn+POO+/EDz/8gOnTp+Pdd9/Frl270NjYiJdeegmAuG/K\ngAED8Nlnn+Hee+/FM888A0Ac2YwePRpffvklJk2a1HLRUWlpKWJiYrBz50588cUXuOmmmwAAERER\niImJwZ49ezT9G5O9MfjJUlwuF/Lz8xEWFoaRI0eiubkZeXl5AICUlBRUVlZi3759+Oqrr3DjjTci\nIyMD8+bNa3OnVAA4dOhQyzOhASA1NRV33HEH3n77bYSFhQEA1qxZg/nz5yMjIwNjxozBxYsXcejQ\nIXz88ccoKipCly7iK9SvXz/s3bsXQ4cORUJCAgBg2rRp2LBhQ8vyf/aznwEQNwyrrKwEAGzcuBFT\npkwBAIwfP77l1tCpqalYu3YtZs+ejU2bNqFPnz4tyxkyZEjL/ycKhjk6Nok6oWvXrgDEHQsjIiJa\n3u/SpQsaGxuhKApGjBiBLVu2dLgsz1NcJSUl2LBhAz788EPMmzcPX3zxBQBg+fLlSExM9Pt/gbZ3\nQ1QUpdV73bp1AwCEhYW1dEn5Wg4AJCYmYseOHSgpKcFjjz2GsWPH4vHHH2+ZX+5wiILBTw9ZSiBj\nEa688kocO3YMW7duBSDu3Ojr/uuXX355S9+/oig4dOgQcnJyMH/+fJw+fRrnzp1DXl5eqxOpO3bs\nAADk5ubi5ZdfRlNTEwDg5MmTGDZsGCorK/HNN98AAP7+97/j+uuv91vX0aNH45133gEArF69GidP\nngQg7s/SvXt33HnnnXjkkUdQUVHR8n+OHDmCyy+/vMO/A1F7GPxkWrK17HK5fL72nMfz54iICCxb\ntgy/+93vkJ6ejoyMDHz66adtln/ddde13N62sbERU6dORWpqKjIzM/Hggw8iKioKjz/+OBoaGpCa\nmoqRI0fiiSeeAADcfffduOyyy5Camor09HQsXrwY3bt3x6JFi/CLX/wCqampCA8Pxz333NOmnp7r\n8MQTT2DDhg0YOXIkVqxY0RLoX3zxBbKyspCRkYEnn3yypbXf0NCA6upqJCUlqf8Dk2NxOCc5lhzO\nuW3btpbuI7Nbs2YNSkpK8PzzzxtdFbIwtvjJsVwuF2bOnIm3337b6KoE7LXXXsNDDz1kdDXI4tji\nJyJyGLb4iYgchsFPROQwDH4iIodh8BMROQyDn4jIYRj8REQO8/9AXMq8UQcBKgAAAABJRU5ErkJg\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3f3df50>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Audio\n",
      "Audio(x, rate=44100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "                <audio controls=\"controls\" >\n",
        "                    <source src=\"data:audio/wav;base64,[encoded data, 352860 characters]\" type=\"audio/wav\" />\n",
        "                    Your browser does not support the audio element.\n",
        "                </audio>\n",
        "              "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<IPython.lib.display.Audio at 0x3e4b710>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Windowing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Before extracting features from a frame of audio, we first multiply the frame by a window, such as the Hamming window pictured below, to reduce artifacts caused by the edges of the frame."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(hamming(101))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[<matplotlib.lines.Line2D at 0x569e510>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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aITY2Fl5eXsWPycvLQ0BAADZt2gSdTofc3Fw8cU8DC07RWFdwsOj3zgJP5qQoQMeOQN++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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4426990>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "MFCCs"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Mel-frequency cepstral coefficients (MFCCs) are a set of features that describe the coarse overall shape of a spectrum but not the fine harmonic structure. MFCCs are often used to describe the timbre of a musical signal."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from essentia.standard import MFCC, Spectrum, Windowing, FrameGenerator\n",
      "hamming_window = Windowing(type='hamming')\n",
      "spectrum = Spectrum()  # we just want the magnitude spectrum\n",
      "mfcc = MFCC()\n",
      "\n",
      "mfccs = array([mfcc(spectrum(hamming_window(frame)))[1]\n",
      "               for frame in FrameGenerator(x, frameSize=1024, hopSize=512)])\n",
      "print mfccs.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(260, 13)\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Display MFCCs over time:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "imshow(mfccs[:,1:].T, origin='lower', aspect='auto', interpolation='nearest') # Ignore the 0th MFCC\n",
      "yticks(range(12), range(1,13)) # Ignore the 0th MFCC\n",
      "ylabel('MFCC Coefficient Index')\n",
      "xlabel('Frame Index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.text.Text at 0x569ecd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CAmvkyJG+bd67d6910UUXWUOHDrXKysqsv/71r51cafj84Ac/sPr3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\nyIuIRDEN8iIiUUyDvIhIFNMgLyISxf4/U1M+MF9LxvYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x567c210>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Centroid"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In Essentia, `centroid` can be used to compute either the temporal or spectral centroid of a frame."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from essentia.standard import Centroid, Spectrum, Windowing, FrameGenerator\n",
      "hamming_window = Windowing(type='hamming')\n",
      "spectrum = Spectrum()  # we just want the magnitude spectrum\n",
      "centroid = Centroid(range=22050)\n",
      "\n",
      "energy = array([centroid(spectrum(hamming_window(frame)))\n",
      "                for frame in FrameGenerator(x, frameSize=2048, hopSize=1024)])\n",
      "\n",
      "plot(energy)\n",
      "ylabel('Spectral Centroid')\n",
      "xlabel('Frame Index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x56b2050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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u3aCpSY/9yO2ChCPs9kQOHTrEzJkzGTx4MEOGDOH666/nq6++8kTbuhyjy1kA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\nvXvDn/4Ejz0Gs2bpAffWP/v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       "text": [
        "<matplotlib.figure.Figure at 0x54b7650>"
       ]
      }
     ],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}