aadjones/240E_HW_5

## 240E_HW_5
{
 "metadata": {
  "name": "HW_5"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from essentia.standard import *",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "cd ~/Desktop/Music/",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "/Users/adj/Desktop/Music\n"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "loader = MonoLoader(filename = '03 Waldstein Sonata, I..m4a')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "waldstein = loader()",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "sr = 44100",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plot(waldstein)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": "[<matplotlib.lines.Line2D at 0x11345f5d0>]"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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j5mzeyNHCH35o3Lb8NTSIv0ePlm+GjI8Xnx84MHxZPXvy14d1JiwpIBvR0WHK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\nDQAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x10facbc50>"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "rhythm_extractor = RhythmExtractor2013()",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "bpm, ticks, confidence, estimates, bpmIntervals = rhythm_extractor(waldstein)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "bpm",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": "172.26568603515625"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#actually pretty reasonable!",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "confidence",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": "0.37285304069519043"
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#let's try using the other method (degara)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "rhythm_extractor = RhythmExtractor2013(method = 'degara')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "bpm, ticks, confidence, estimates, bpmIntervals = rhythm_extractor(waldstein)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "bpm",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": "172.26551818847656"
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#extremely similar!",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#this movement is very rhythmic (at least in the first half of the exposition), so that probably made the calculations more reliable.\n#Let's try something very free in contrast.",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "loader = MonoLoader(filename = '04 Organum.m4a')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "organum = loader()",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "rhythm_extractor_multifeature = RhythmExtractor2013()     #multifeature is used by default\nbpm, ticks, confidence, estimates, bpmIntervals = rhythm_extractor_multifeature(organum)\nprint(bpm)\nprint(confidence)\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "117.853820801\n-0.1963750422\n"
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Not to pick on the machine but a human would never identify this piece as having a tempo of 117 (there isn't really a beat,\n#so there would be no well-defined tempo. At least the confidence is very low! Let's try the other method.",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "rhythm_extractor_degara = RhythmExtractor2013(method = 'degara')\nbpm, ticks, confidence, estimates, bpmIntervals = rhythm_extractor_degara(organum)\nprint(bpm)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "139.726806641\n"
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Here we see even the two different methods varying considerably from each other.",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Let's try something in the middle---next we will use a Romantic piano piece with different sections and rubato.",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "loader = MonoLoader(filename = '06 Character piece.m4a')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "character_piece = loader()",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "rhythm_extractor_multifeature = RhythmExtractor2013()\nbpm, ticks, confidence, estimates, bpmIntervals = rhythm_extractor_multifeature(character_piece)\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "bpm",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": "106.04551696777344"
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "confidence",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": "0.10979857295751572"
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#this sort of makes sense for parts of the piece, but not for all of it. we do see a pretty low confidence.\n#for instance, let's see what we might have gotten if we had just taken the first 10 seconds of the piece.",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "character_piece = character_piece[:441000]\nrhythm_extractor_multifeature = RhythmExtractor2013()\nbpm, ticks, confidence, estimates, bpmIntervals = rhythm_extractor_multifeature(character_piece)\nprint(bpm)\nprint(confidence)\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "116.801330566\n2.80740118027\n"
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#a bit different already!",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Let's try our own homebrewn tempo estimator, following the code from class. We'll use the first 10 seconds of character_piece.",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def windowed_rms(input_sig, win_size, hop=None, sr=1.0):\n    if not hop:\n        hop = winsize/2\n    rms = []\n    window_start = arange(0, len(input_sig), hop)\n    \n    for start in window_start:\n        w = input_sig[start: start+win_size].astype(float)\n        rms_inst = sqrt(mean(w**2))\n        rms.append(rms_inst)\n    times = (window_start + win_size/2)/float(sr)\n    return times, rms",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "times, super_rms = windowed_rms(character_piece, 4096, 512, 44100)\nplot(times, super_rms)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": "[<matplotlib.lines.Line2D at 0x111ba7790>]"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7bLaUJRkdzYTE1WcPAF/9KpCVBdTUAM8/z0IxxeCsbiF3GHfDELqJR0ezZGZS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\n05ZFUtEAAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0x111b4e6d0>"
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "lags, cc, lines, line = acorr(super_rms, maxlags=600)\ngrid();",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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P/vXXX2dxJ3JCZWVlWLlypVVjsAdvjxXV8VuoqKgI7u7uMBqNNkhFRGrXokUL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\n",
       "text": "<matplotlib.figure.Figure at 0x113446f90>"
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "lags[argmax(cc[625:]) + 625]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": "25"
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#This of course tells us nothing other than that it is correlated at zero lag. Not very informative! Let's try Waldstein instead.",
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "waldstein = waldstein[:441000]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "times, super_rms = windowed_rms(waldstein, 4096, 512, 44100)\nplot(times, super_rms)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": "[<matplotlib.lines.Line2D at 0x11448c250>]"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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V29lymAzUjAIWgbNngbvvpjWFuUPjoLC6nTds2gT87nc0Yv+rvwpc+wHtpk5I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\nIAgRjIiAIAhCBCMiIAiCEMH8fz00qTc9+7V8AAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x111ba7650>"
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "lags, cc, lines, line = acorr(super_rms, maxlags=600)\ngrid();",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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giUjV2IMnIiLVYYEnIlVjD96MpKQkBAcHIygoCLGxsQ2OWbBgAYKCghAWFoaT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\n+n/nxE3rNay2fAAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x111b8a290>"
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "lags[argmax(cc[625:]) + 625]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": "29"
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "float(sr) / 29",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": "1520.6896551724137"
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "1.0 / times[29]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": "2.6100852272727271"
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "bpm = 60.0 * _\nprint bpm",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "156.605113636\n"
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#That's pretty good, although a bit on the slow side compared to Essentia. Going back to listen,\n#I think Essentia was slightly more accurate.\n\n#Overall, the tempo estimation is pretty powerful, although as expected, for classical music that has a freer, more\n#fluid tempo, it is not great for obvious reasons.",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}