src/examples/tutorial/essentia_python_tutorial.ipynb

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    "# Essentia Python tutorial\n",
    "\n",
    "This is a hands-on tutorial for complete newcomers to Essentia. Essentia combines the power of computation speed of the main C++ code with the Python environment which makes fast prototyping and scientific reseach very easy.\n",
    "\n",
    "First and foremost, if you are a newbie to Python, we recommend you to use [Ipython](https://ipython.org/) interactive shell instead of the standard python interpreter. Optionally, if you are familiar with python notebooks, you may want to use one created for this tutorial for a more interactive experience. It can be found in the ```src/examples/tutorial``` folder in the ```essentia_python_tutorial.ipynb``` file. Read how to use python notebooks [here](http://jupyter.readthedocs.io). \n",
    "\n",
    "\n",
    "You should have the [NumPy](http://numpy.scipy.org/) package installed, which gives Python the ability to work with vectors and matrices in pretty much the same way as Matlab. You can also install [SciPy](http://www.scipy.org/), which provides functionality similar to Matlab’s toolboxes, although we won’t be using it in this tutorial. You should have the [matplotlib](http://matplotlib.sourceforge.net/) package installed if you want to be able to do some plotting. Other recommended packages include [scikit-learn](http://scikit-learn.org/) for data analysis and machine learning and [seaborn](https://stanford.edu/~mwaskom/software/seaborn/) for visualization. \n",
    "\n",
    "\n",
    "The big strength of Essentia is in its considerably large collection of algorithms for audio processing and analysis which have been optimized and tested and which you can rely on to build your own signal analysis. That is, often you do not have to chase around lots of toolboxes to be able to achieve what you want. For more details on the algorithms, have a look either at the [algorithms overview](http://essentia.upf.edu/documentation/algorithms_overview.html) or at the [complete reference](http://essentia.upf.edu/documentation/algorithms_reference.html)."
   ]
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   "source": [
    "## Using Essentia in standard mode"
   ]
  },
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   "source": [
    "In this section we will focus on how to use Essentia in the *standard mode* (think Matlab). There is another section that you can read afterwards about using the streaming mode. \n",
    "\n",
    "We will have a look at some basic functionality:\n",
    "\n",
    " - how to load an audio\n",
    " - how to perform some numerical operations such as FFT\n",
    " - how to plot results\n",
    " - how to output results to a file\n",
    " \n",
    "**Note**: all the following commands need to be typed in a python interpreter. It is highly recommended to use IPython, and to start it with the ```--pylab``` option to have interactive plots.\n",
    "\n",
    "### Exploring the python module"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s investigate a bit the Essentia package."
   ]
  },
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     "text": [
      "['AfterMaxToBeforeMaxEnergyRatio', 'AllPass', 'AudioLoader', 'AudioOnsetsMarker', 'AudioWriter', 'AutoCorrelation', 'BPF', 'BandPass', 'BandReject', 'BarkBands', 'BeatTrackerDegara', 'BeatTrackerMultiFeature', 'Beatogram', 'BeatsLoudness', 'BinaryOperator', 'BinaryOperatorStream', 'BpmHistogram', 'BpmHistogramDescriptors', 'BpmRubato', 'CartesianToPolar', 'CentralMoments', 'Centroid', 'ChordsDescriptors', 'ChordsDetection', 'ChordsDetectionBeats', 'Chromagram', 'Clipper', 'ConstantQ', 'Crest', 'CrossCorrelation', 'CubicSpline', 'DCRemoval', 'DCT', 'Danceability', 'Decrease', 'Derivative', 'DerivativeSFX', 'Dissonance', 'DistributionShape', 'Duration', 'DynamicComplexity', 'ERBBands', 'EasyLoader', 'EffectiveDuration', 'Energy', 'EnergyBand', 'EnergyBandRatio', 'Entropy', 'Envelope', 'EqloudLoader', 'EqualLoudness', 'Extractor', 'FFT', 'FFTC', 'FadeDetection', 'Flatness', 'FlatnessDB', 'FlatnessSFX', 'Flux', 'FrameCutter', 'FrameGenerator', 'FrameToReal', 'FrequencyBands', 'GFCC', 'GeometricMean', 'HFC', 'HPCP', 'HarmonicBpm', 'HarmonicMask', 'HarmonicModelAnal', 'HarmonicPeaks', 'HighPass', 'HighResolutionFeatures', 'HprModelAnal', 'HpsModelAnal', 'IFFT', 'IIR', 'Inharmonicity', 'InstantPower', 'Intensity', 'Key', 'KeyExtractor', 'LPC', 'Larm', 'Leq', 'LevelExtractor', 'LogAttackTime', 'LoopBpmConfidence', 'LoopBpmEstimator', 'Loudness', 'LoudnessEBUR128', 'LoudnessVickers', 'LowLevelSpectralEqloudExtractor', 'LowLevelSpectralExtractor', 'LowPass', 'MFCC', 'Magnitude', 'MaxFilter', 'MaxMagFreq', 'MaxToTotal', 'Mean', 'Median', 'MelBands', 'MetadataReader', 'Meter', 'MinToTotal', 'MonoLoader', 'MonoMixer', 'MonoWriter', 'MovingAverage', 'MultiPitchKlapuri', 'MultiPitchMelodia', 'Multiplexer', 'NoiseAdder', 'NoveltyCurve', 'NoveltyCurveFixedBpmEstimator', 'OddToEvenHarmonicEnergyRatio', 'OnsetDetection', 'OnsetDetectionGlobal', 'OnsetRate', 'Onsets', 'OverlapAdd', 'PCA', 'Panning', 'PeakDetection', 'PercivalBpmEstimator', 'PercivalEnhanceHarmonics', 'PercivalEvaluatePulseTrains', 'PitchContourSegmentation', 'PitchContours', 'PitchContoursMelody', 'PitchContoursMonoMelody', 'PitchContoursMultiMelody', 'PitchFilter', 'PitchMelodia', 'PitchSalience', 'PitchSalienceFunction', 'PitchSalienceFunctionPeaks', 'PitchYin', 'PitchYinFFT', 'PolarToCartesian', 'PoolAggregator', 'PowerMean', 'PowerSpectrum', 'PredominantPitchMelodia', 'RMS', 'RawMoments', 'ReplayGain', 'Resample', 'ResampleFFT', 'RhythmDescriptors', 'RhythmExtractor', 'RhythmExtractor2013', 'RhythmTransform', 'RollOff', 'SBic', 'Scale', 'SilenceRate', 'SineModelAnal', 'SineModelSynth', 'SineSubtraction', 'SingleBeatLoudness', 'SingleGaussian', 'Slicer', 'SpectralCentroidTime', 'SpectralComplexity', 'SpectralContrast', 'SpectralPeaks', 'SpectralWhitening', 'Spectrum', 'Spline', 'SprModelAnal', 'SprModelSynth', 'SpsModelAnal', 'SpsModelSynth', 'StartStopSilence', 'StereoDemuxer', 'StochasticModelAnal', 'StochasticModelSynth', 'StrongDecay', 'StrongPeak', 'SuperFluxExtractor', 'SuperFluxNovelty', 'SuperFluxPeaks', 'TCToTotal', 'TempoScaleBands', 'TempoTap', 'TempoTapDegara', 'TempoTapMaxAgreement', 'TempoTapTicks', 'TonalExtractor', 'TonicIndianArtMusic', 'TriangularBands', 'Trimmer', 'Tristimulus', 'TuningFrequency', 'TuningFrequencyExtractor', 'UnaryOperator', 'UnaryOperatorStream', 'Variance', 'Vibrato', 'WarpedAutoCorrelation', 'Windowing', 'YamlInput', 'YamlOutput', 'ZeroCrossingRate', '__builtins__', '__doc__', '__file__', '__name__', '__package__', '_c', '_create_essentia_class', '_create_python_algorithms', '_essentia', '_reloadAlgorithms', '_sys', 'algorithmInfo', 'algorithmNames', 'essentia']\n"
     ]
    }
   ],
   "source": [
    "# first, we need to import our essentia module. It is aptly named 'essentia'!\n",
    "import essentia\n",
    "\n",
    "# as there are 2 operating modes in essentia which have the same algorithms,\n",
    "# these latter are dispatched into 2 submodules:\n",
    "import essentia.standard\n",
    "import essentia.streaming\n",
    "\n",
    "# let's have a look at what is in there\n",
    "print dir(essentia.standard)\n",
    "\n",
    "# you can also do it by using autocompletion in IPython, typing \"essentia.standard.\" and pressing Tab\n",
    "    "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This list contains all Essentia algorithms available in standard mode. You can have an inline help for the algorithms you are interested in using ```help``` command (you can also see it by typing \"*MFCC?*\" in IPython). You can also use our online [algorithm reference](http://essentia.upf.edu/documentation/algorithms_reference.html)."
   ]
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     "text": [
      "Help on class Algo in module essentia.standard:\n",
      "\n",
      "class Algo(Algorithm)\n",
      " |  MFCC\n",
      " |  \n",
      " |  \n",
      " |  Inputs:\n",
      " |  \n",
      " |    [vector_real] spectrum - the audio spectrum\n",
      " |  \n",
      " |  \n",
      " |  Outputs:\n",
      " |  \n",
      " |    [vector_real] bands - the energies in mel bands\n",
      " |    [vector_real] mfcc - the mel frequency cepstrum coefficients\n",
      " |  \n",
      " |  \n",
      " |  Parameters:\n",
      " |  \n",
      " |    highFrequencyBound:\n",
      " |      real ∈ (0,inf) (default = 11000)\n",
      " |      the upper bound of the frequency range [Hz]\n",
      " |  \n",
      " |    inputSize:\n",
      " |      integer ∈ (1,inf) (default = 1025)\n",
      " |      the size of input spectrum\n",
      " |  \n",
      " |    lowFrequencyBound:\n",
      " |      real ∈ [0,inf) (default = 0)\n",
      " |      the lower bound of the frequency range [Hz]\n",
      " |  \n",
      " |    numberBands:\n",
      " |      integer ∈ [1,inf) (default = 40)\n",
      " |      the number of mel-bands in the filter\n",
      " |  \n",
      " |    numberCoefficients:\n",
      " |      integer ∈ [1,inf) (default = 13)\n",
      " |      the number of output mel coefficients\n",
      " |  \n",
      " |    sampleRate:\n",
      " |      real ∈ (0,inf) (default = 44100)\n",
      " |      the sampling rate of the audio signal [Hz]\n",
      " |  \n",
      " |  \n",
      " |  Description:\n",
      " |  \n",
      " |    This algorithm computes the mel-frequency cepstrum coefficients of a\n",
      " |    spectrum. As there is no standard implementation, the MFCC-FB40 is used by\n",
      " |    default:\n",
      " |      - filterbank of 40 bands from 0 to 11000Hz\n",
      " |      - take the log value of the spectrum energy in each mel band\n",
      " |      - DCT of the 40 bands down to 13 mel coefficients\n",
      " |    There is a paper describing various MFCC implementations [1].\n",
      " |    \n",
      " |    This algorithm depends on the algorithms MelBands and DCT and therefore\n",
      " |    inherits their parameter restrictions. An exception is thrown if any of these\n",
      " |    restrictions are not met. The input \"spectrum\" is passed to the MelBands\n",
      " |    algorithm and thus imposes MelBands' input requirements. Exceptions are\n",
      " |    inherited by MelBands as well as by DCT.\n",
      " |    \n",
      " |    References:\n",
      " |      [1] T. Ganchev, N. Fakotakis, and G. Kokkinakis, \"Comparative evaluation\n",
      " |      of various MFCC implementations on the speaker verification task,\" in\n",
      " |      International Conference on Speach and Computer (SPECOM’05), 2005, vol.\n",
      " |    1,\n",
      " |      pp. 191–194.\n",
      " |    \n",
      " |      [2] Mel-frequency cepstrum - Wikipedia, the free encyclopedia,\n",
      " |      http://en.wikipedia.org/wiki/Mel_frequency_cepstral_coefficient\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      Algo\n",
      " |      Algorithm\n",
      " |      __builtin__.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __call__(self, *args)\n",
      " |  \n",
      " |  __init__(self, **kwargs)\n",
      " |  \n",
      " |  __str__(self)\n",
      " |  \n",
      " |  compute(self, *args)\n",
      " |  \n",
      " |  configure(self, **kwargs)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __struct__ = {'category': 'Spectral', 'description': 'This algorithm c...\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from Algorithm:\n",
      " |  \n",
      " |  __compute__(...)\n",
      " |      compute the algorithm\n",
      " |  \n",
      " |  __configure__(...)\n",
      " |      Configure the algorithm\n",
      " |  \n",
      " |  getDoc(...)\n",
      " |      Returns the doc string for the algorithm\n",
      " |  \n",
      " |  getStruct(...)\n",
      " |      Returns the doc struct for the algorithm\n",
      " |  \n",
      " |  inputNames(...)\n",
      " |      Returns the names of the inputs of the algorithm.\n",
      " |  \n",
      " |  inputType(...)\n",
      " |      Returns the type of the input given by its name\n",
      " |  \n",
      " |  name(...)\n",
      " |      Returns the name of the algorithm.\n",
      " |  \n",
      " |  outputNames(...)\n",
      " |      Returns the names of the outputs of the algorithm.\n",
      " |  \n",
      " |  paramType(...)\n",
      " |      Returns the type of the parameter given by its name\n",
      " |  \n",
      " |  paramValue(...)\n",
      " |      Returns the value of the parameter or None if not yet configured\n",
      " |  \n",
      " |  parameterNames(...)\n",
      " |      Returns the names of the parameters for this algorithm.\n",
      " |  \n",
      " |  reset(...)\n",
      " |      Reset the algorithm to its initial state (if any).\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from Algorithm:\n",
      " |  \n",
      " |  __new__ = <built-in method __new__ of type object>\n",
      " |      T.__new__(S, ...) -> a new object with type S, a subtype of T\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(essentia.standard.MFCC)"
   ]
  },
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   "metadata": {},
   "source": [
    "### Instantiating our first algorithm, loading some audio\n",
    "\n",
    "Before you can use algorithms in Essentia, you first need to instantiate (create) them. When doing so, you can give them parameters which they may need to work properly, such as the filename of the audio file in the case of an audio loader.\n",
    "\n",
    "Once you have instantiated an algorithm, nothing has happened yet, but your algorithm is ready to be used and works like a function, that is, *you have to call it to make stuff happen* (technically, it is a [function object]( <http://en.wikipedia.org/wiki/Function_object>)).\n",
    "\n",
    "Essentia has a selection of audio loaders:\n",
    "\n",
    "- [AudioLoader](http://essentia.upf.edu/documentation/reference/std_AudioLoader.html): the most generic one, returns the audio samples, sampling rate and number of channels, and some other related information\n",
    "- [MonoLoader](http://essentia.upf.edu/documentation/reference/std_MonoLoader.html): returns audio, down-mixed and resampled to a given sampling rate\n",
    "- [EasyLoader](http://essentia.upf.edu/documentation/reference/std_EasyLoader.html): a MonoLoader which can optionally trim start/end slices and rescale according to a ReplayGain value\n",
    "- [EqloudLoader](http://essentia.upf.edu/documentation/reference/std_EqloudLoader.html): an EasyLoader that applies an equal-loudness filtering to the audio\n"
   ]
  },
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   "source": [
    "# we start by instantiating the audio loader:\n",
    "loader = essentia.standard.MonoLoader(filename='../../../test/audio/recorded/dubstep.wav')\n",
    "\n",
    "# and then we actually perform the loading:\n",
    "audio = loader()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the MonoLoader will output audio with 44100Hz samplerate downmixed to mono. To make sure that this actually worked, let's plot a 1-second slice of audio, from t = 1 sec to t = 2sec:"
   ]
  },
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JU2XYJkvPxhsDZ53lv40pzfKlY9EC+HHaacDDD6veS1v0h3XCBOCRR9Kd3/Yj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11d44b4e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pylab contains the plot() function, as well as figure, etc... (same names as Matlab)\n",
    "from pylab import plot, show, figure, imshow\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (15, 6) # set plot sizes to something larger than default\n",
    "\n",
    "plot(audio[1*44100:2*44100])\n",
    "plt.title(\"This is how the 2nd second of this audio looks like:\")\n",
    "show() # unnecessary if you started \"ipython --pylab\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that if you have started IPython with the ``--pylab`` option, the call to\n",
    "show() is not necessary, and you don't have to close the plot to regain control of your terminal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing spectrum, mel bands energies and MFCCs\n",
    "So let's say that we want to compute spectral energy in mel bands and the associated [MFCCs](http://en.wikipedia.org/wiki/Mel-frequency_cepstral_coefficient) for the frames in our audio.\n",
    "\n",
    "We will need the following algorithms: Windowing, Spectrum, MFCC. For windowing we'll specify to use Hann window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "  from essentia.standard import *\n",
    "  w = Windowing(type = 'hann')\n",
    "  spectrum = Spectrum()  # FFT() would return the complex FFT, here we just want the magnitude spectrum\n",
    "  mfcc = MFCC()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once algorithms have been instantiated, they work like normal functions. Note that the MFCC algorithm returns two values: the band energies and the coefficients, and that you can get (unpack) them the same way as in Matlab. Let's compute and plot the spectrum, mel band energies, and MFCCs for a frame of audio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BnGRmKyR9TtLPzOytzOZHSnrVzGZLelrSzfnBrpBiA6pEo2Xu2kXlDgAAAADi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\nmoksXSqSkBBa3qZMEUlNdT526RKf/913oZ+1ZAnrmzlz+Fx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C4HGlVGLoqwxXAVNBmbAz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\nDAaDwWAoAvw/wIfr90/NddYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11d46844d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IMyJiFnAF8OWGff4RuKq8fDnw7fLyJsrxcxExF3g+cFfbVVas11vmli93zJwk\nSZI0aMYMc+UYuLcA1wN3AKszc01EXBsRryh3+wiwJCLWAv8JqC1B8FfAvIi4Hfgh8JHMvL3TT6Lb\nenWNuZoTTywmaXmk76aWkSRJkjRekZlV10BEZC/UMZrzz4ePfAQuuqjqSkZ39tnwhS/AOedUXYkk\nSZKkdkUEmRlj73lYS4uGD7LM3h8zBy5PIEmSJA0aw9wYHnoIZs8uujL2MpcnkCRJkgaLYW4MvT5e\nrsaWOUmSJGmwGObG0OszWda4PIEkSZI0WAxzY+iH8XLg8gSSJEnSoDHMjaGfWuYMc5IkSdLgMMyN\noV/GzDkBiiRJkjRYDHNj6JeWuQULimUUdu+uuhJJkiRJk8EwdwyPPAJ79sApp1RdydgibJ2TJEmS\nBolh7hhqrXLR1jrs1XHcnCRJkjQ4DHPH0C9dLGtcnkCSJEkaHIa5Y+i3MOfyBJIkSdLgMMwdQ7+s\nMVdjN0tJkiRpcBjmjqFfliWocQIUSZIkaXAY5o6h37pZ2jInSZIkDY7IzKprICKyF+qot38/zJ9f\nLE0wY0bV1bRm925YtgwefbR/ZuCUJEmSBBFBZrb1Ld6WuVFs2FC0dPVLkINi4fBp01w4XJIkSRoE\nhrlR9Nt4uRqXJ5AkSZIGg2FuFP02Xq7G5QkkSZKkwWCYG0W/hjknQZEkSZIGg2FuFP22xlyNyxNI\nkiRJg8EwN4p+HjO3aVPVVUiSJEnqNsNcEyMjcN99sHJl1ZW075nPhDvuqLoKSZIkSd1mmGti61ZY\nvBjmzKm6kvadd14R5g4cqLoSSZIkSd1kmGuiX8fLAZxwAqxYAWvWVF2JJEmSpG4yzDXRr+Plai64\nAH7846qrkCRJktRNhrkm+nVZghrDnCRJkjT1Geaa6OdulmCYkyRJkgaBYa6JqdAyd8stcOhQ1ZVI\nkiRJ6hbDXIPM/h8zt2QJzJ8PGzZUXYkkSZKkbjHMNdi+HWbMgIULq65kYuxqKUmSJE1thrkG/T5e\nrsYwJ0mSJE1tLYW5iLg0Iu6KiHsi4m1Nbp8VEasjYm1EfD8iVtTddl5EfC8ibo+IWyNiViefQKf1\n+3i5GsOcJEmSNLWNGeYiYhrwQeDlwLnAlRFxdsNubwJ2ZObTgPcB7y7vOx34O+DfZeazgCHgQMeq\n74J+Hy9XY5iTJEmSprZWWuYuBtZm5sbMPACsBi5r2Ocy4Lry8ueAl5aXfx64NTNvB8jMnZmZEy+7\ne6ZKy9x+k2cpAAAbyElEQVSKFbB/PzzwQNWVSJIkSeqGVsLcMmBz3fUt5bam+2TmCLA7IhYBTweI\niG9ExI8i4g8nXnJ3TZUxcxFw4YW2zkmSJElTVbcmQInyfAbwQuBK4EXAqyLiJV16zI6YKt0swa6W\nkiRJ0lQ2o4V9tgIr6q4vL7fV2wKcDtxfjpObn5k7ImIL8M+ZuRMgIr4GXAh8p/FBVq1a9eTloaEh\nhoaGWn8WHfLoo8Xp1FMn/aG74oIL4ItfrLoKSZIkSY2Gh4cZHh6e0DFirCFsZTi7G3gZsA24Abgy\nM9fU7XM18KzMvDoirgB+JTOviIgTgX8Cfg44CHwdeE9mfr3hMXpiKN2tt8Kv/RrcfnvVlXTGmjXw\ny79cdB2VJEmS1LsigsyMsfc8bMyWucwciYi3ANdTdMv8SGauiYhrgRsz8yvAR4C/i4i1wMPAFeV9\nd0XEe4AfAYeArzYGuV4yVcbL1Tz96cUEKLt3w4IFVVcjSZIkqZNa6WZJZn4DeEbDtmvqLu8HXjvK\nfT8NfHoCNU6aqTReDmD6dHj2s4sWxxe/uOpqJEmSJHVStyZA6UtTZVmCek6CIkmSJE1Nhrk6hjlJ\nkiRJ/cIwV2eqjZkDw5wkSZI0VY05m+WkFNEDs1k+8QTMmwePPQYzZ1ZaSkc9/jgsWgQ7d8Ls2VVX\nI0mSJKmZ8cxmactc6b77YPnyqRXkAI47rpjUZaostyBJkiSpYJgrTcXxcjV2tZQkSZKmHsNcaSqO\nl6sxzEmSJElTj2GuNNXWmKtnmJMkSZKmHsNcaSp3szz/fPjJT2BkpOpKJEmSJHWKYa40lcPcggWw\ndCmsXVt1JZIkSZI6xTAHHDoEGzbAypVVV9I9drWUJEmSphbDHLB1KyxcCHPnVl1J9xjmJEmSpKnF\nMMfU7mJZY5iTJEmSphbDHIMV5jKrrkSSJElSJxjmmNprzNWceirMmAFbtlRdiSRJkqROMMwxtdeY\nq3fBBXDzzVVXIUmSJKkTDHMMRjdLcNycJEmSNJUMfJjLNMxJkiRJ6j8DH+YefhgiYNGiqivpPsOc\nJEmSNHUMfJirtcpFVF1J961cCbt3FwFWkiRJUn8zzA1IF0uAadPgOc+xdU6SJEmaCgY+zA3CsgT1\n7GopSZIkTQ0DH+YGZVmCGsOcJEmSNDUY5gaomyUY5iRJkqSpwjA3YGHunHNg40bYs6fqSiRJkiRN\nxECHucceg1274LTTqq5k8sycCc98Jtx2W9WVSJIkSZqIgQ5z69cX0/VPG7BXwa6WkiRJUv8bsBhz\npEHrYlljmJMkSZL6n2HOMCdJkiSpDw10mBu0NeZqzjsP7rwTDhyouhJJkiRJ4zXQYW7Q1pirOeEE\nWLEC1qypuhJJkiRJ4zXwYW4QW+bArpaSJElSvxvYMPfEE7B1K5xxRtWVVMMwJ0mSJPW3lsJcRFwa\nEXdFxD0R8bYmt8+KiNURsTYivh8RKxpuXxERj0bEH3Sq8InauLFYX27WrKorqYZhTpIkSepvY4a5\niJgGfBB4OXAucGVEnN2w25uAHZn5NOB9wLsbbv8L4GsTL7dzBnW8XM0FF8Att8ChQ1VXIkmSJGk8\nWmmZuxhYm5kbM/MAsBq4rGGfy4DrysufA15WuyEiLgPWA3dMvNzOGeTxcgBLlsD8+bBhQ9WVSJIk\nSRqPVsLcMmBz3fUt5bam+2TmCLArIhZFxFzgvwDXAjHxcjtn0MMc2NVSkiRJ6mfdmgClFtxWAe/N\nzL0N2ys3qGvM1TPMSZIkSf1rRgv7bAXqJzRZXm6rtwU4Hbg/IqYD8zNzR0Q8D/jViHg3sBAYiYh9\nmfmhxgdZtWrVk5eHhoYYGhpq53m0bdDHzEER5j784aqrkCRJkgbP8PAww8PDEzpGZOaxdyjC2d0U\n4+C2ATcAV2bmmrp9rgaelZlXR8QVwK9k5hUNx7kGeDQz39PkMXKsOjrp0KFi4eyHHirOB9WmTfC8\n58G2bVVXIkmSJA22iCAz2+rJOGY3y3IM3FuA6ykmMVmdmWsi4tqIeEW520eAJRGxFvhPwNvbK31y\nbdtWTP4xyEEO4PTTi/X2Hnig6kokSZIktauVbpZk5jeAZzRsu6bu8n7gtWMc49rxFNgNjpcrRBwe\nN/cLv1B1NZIkSZLa0a0JUHqa4+UOcxIUSZIkqT8NbJizZa5gmJMkSZL6k2FuwBnmJEmSpP40kGHu\n3nvtZlnz9KcXE6Ds3l11JZIkSZLaMZBhzpa5w6ZPh2c/G269tepKJEmSJLVj4MLcjh3FOnOLF1dd\nSe+wq6UkSZLUfwYuzNVa5aKt5fimtgsugJtvrroKSZIkSe0YuDDneLmj2TInSZIk9Z+BC3OOlzva\ns54Fa9fC449XXYkkSZKkVhnmxHHHwdOeBrffXnUlkiRJklplmBNgV0tJkiSp3wxcmHPMXHOGOUmS\nJKm/DFSY27sXdu6EZcuqrqT3GOYkSZKk/jJQYW79ejjzTJg2UM+6NeefDz/5CYyMVF2JJEmSpFYM\nVKy5917Hy41mwQI4+WS4556qK5EkSZLUioEKc+vWOV7uWOxqKUmSJPWPgQtztsyNzjAnSZIk9Q/D\nnJ5kmJMkSZL6x0CFOcfMHVstzGVWXYkkSZKksQxMmDtwALZsKWazVHOnngozZ8LmzVVXIkmSJGks\nAxPmNm0qwsrs2VVX0tvsailJkiT1h4EJc46Xa41hTpIkSeoPAxPmHC/XGsOcJEmS1B8GJsy5xlxr\nDHOSJElSfxioMGfL3NhWroTdu+Hhh6uuRJIkSdKxGOZ0hGnT4DnPsXVOkiRJ6nUDEeYyDXPtsKul\nJEmS1PsGIsxt2wbz5hUnjc0wJ0mSJPW+gQhztsq1xzAnSZIk9T7DnI5yzjmwcSPs2VN1JZIkSZJG\nMxBh7t57XZagHTNnwjOfCbfdVnUlkiRJkkYzEGHOlrn22dVSkiRJ6m2GOTVlmJMkSZJ6W0thLiIu\njYi7IuKeiHhbk9tnRcTqiFgbEd+PiBXl9n8TET+KiFsj4saIeEmnn0ArDHPtM8xJkiRJvS0y89g7\nREwD7gFeBtwP3AhckZl31e3zH4BnZ+bVEfE64FWZeUVEPAd4MDMfiIhzgW9m5vImj5Fj1TFeO3fC\nGWfA7t0Q0ZWHmJIeewyWLi1et5kzq65GkiRJmtoigsxsK7G00jJ3MbA2Mzdm5gFgNXBZwz6XAdeV\nlz9HEfzIzFsz84Hy8h3AcRExqdGg1ipnkGvPCSfAihWwZk3VlUiSJElqppUwtwzYXHd9S7mt6T6Z\nOQLsiohF9TtExGuAm8tAOGnsYjl+drWUJEmSele3JkA5oh2s7GL5p8C/69LjjcowN36GOUmSJKl3\nzWhhn63Airrry8tt9bYApwP3R8R0YH5m7gCIiOXAF4Bfz8z7RnuQVatWPXl5aGiIoaGhFkob2733\nwgte0JFDDZwLLoCvfrXqKiRJkqSpZ3h4mOHh4Qkdo5UJUKYDd1OMg9sG3ABcmZlr6va5GnhWOQHK\nFcCvlBOgnAgMA6sy8x+O8RhdmwDlkkvgmmvgpS/tyuGntO3bYeVK2LULpg3EIhaSJElSNboyAUo5\nBu4twPXAHcDqzFwTEddGxCvK3T4CLImItcB/At5ebv8d4KnAH0XEjyPi5ohY0k6BE2U3y/FbsgQW\nLID166uuRJIkSVKjMVvmJqWILrXM7dsHCxfCnj0wfXrHDz8QXvlK+PVfh8svr7oSSZIkaerq1tIE\nfWv9ejjzTIPcRDgJiiRJktSbpnSYs4vlxF14oWFOkiRJ6kWGOR2TLXOSJElSbzLM6ZhOPx0OHIBt\n26quRJIkSVK9KR3m7r0Xzjqr6ir6W4Stc5IkSVIvamXR8L5ly1xnXHABfPzjsHcvnHxycTrlFJg3\nrwh7kiRJkibflF2a4OBBmDsXHnkEZs/u6KEHzu23w1/9FTz44JGngwcPh7vG0ymnHHl9wQKDnyRJ\nkjSa8SxNMGXD3Pr1MDQEmzZ19LCqs2fP0QGv/vTAA4cvP/EELF16ZMC76ip48YurfhaSJElS9cYT\n5qZsN8t16xwv121z58LKlcVpLPv2HRn01q+H17wGvvc93ydJkiRpPKZ0mHO8XO+YM6dYwP3MMw9v\nmzkTXv1q+MEP4Pjjq6pMkiRJ6k9TdjZLw1zvu/pqeM5z4M1vhh7o7StJkiT1lSkb5lyWoPdFwP/8\nn3DbbcUEK5IkSZJaZzdLVer44+ELX4AXvAAuvBB+9merrkiSJEnqD1OyZS6zmGDDMNcfnvpU+OhH\n4bWvLWbAlCRJkjS2KRnmbryxaPGZP7/qStSqV7wCfuu34HWvgwMHqq5GkiRJ6n1TLsx985tFMPgf\n/6PqStSua64pZr18xzuqrkSSJEnqfVMqzH30o8VC1F/4Avzqr1Zdjdo1fTp8+tPw+c/DZz9bdTWS\nJElSb4vsgTnhIyInUkcmrFoFn/wkfO1r8IxndK42Tb6bb4aXvxy++10455yqq5EkSZK6LyLIzGjn\nPn3fMvfEE/Cbvwlf/zp873sGuangwgvh3e8uFhR/5JGqq5EkSZJ6U1+3zD3ySNGdcs4c+MxnYO7c\nLhSnyrz5zbB9O3zuc8WadJIkSdJUNVAtc1u2wIteBE9/Onzxiwa5qegDH4DNm+HP/7zqSiRJkqTe\n05dh7rbbisWl3/AG+OAHi4kzNPXMnl20yv3FX8C3v111NZIkSVJv6btulv/0T/D61xetNldc0eXC\n1BO+9a0iuN9wA5x+etXVSJIkSZ035btZXncd/NqvFa01BrnB8bKXwe/9Hlx+OezfX3U1kiRJUm/o\ni5a5TPhv/61YR+5rX4NnPnMSi1NPyCxmtzz1VPjQh6quRpIkSeqsKdkyd+AA/PZvwz/8A3z/+wa5\nQRUBH/940eXyuuuqrkaSJEmq3oyqCziWRx8tutZNn14sIH3CCVVXpCotWABf+AIMDcF558EFF1Rd\nkSRJklSdnm2Zu/9+ePGL4Ywz4EtfMsipcO658Jd/WawvuGNH1dVIkvpRJmzdCsPD/i2R1N96cszc\nHXfAL/4i/Pt/D29/uwtG62i///tw993wla/AtJ79l4QkqWqZxT+Ib7oJfvSj4vymm2BkBJ761OI7\nx8qVcMklxenFL4aTTqq6akmDaDxj5nouzH3nO/C618F731vMXCk1c+BAMcvly14G11xTdTWSpF5Q\na3GrBbb64HbRRYdPz31usdRNRPH35KabiuEc3/0u/Ou/wvLlRZf+WsA7+eSqn5mkQdD3Ye5Tn4I/\n+ANYvRpe8pKqq1Kve+CB4g/yhz9ctORKkgZHY3CrtbplHhncLrrocHBrxcGDcMstRRfM734X/uVf\nijBXC3aXXALLlnX1qUkaUH0d5v7kT5IPfxi++tViXJTUin/5l2L83Pe/X3STkSRNLZmwdy889BDc\neuuRLW5wdHBbvryzwzNGRuC22w633P3zP8OiRUeGuxUrOvd4kgZXX4e5889PvvpVOO20qqtRv/nA\nB4o1CL/3PTj++KqrkaTekwm7dsFPf1qEop/+9PDl7duLsccnnABz5x59Gm377NnthaaDB4saduyA\nnTuL8/rLzbbVLk+fDosXw7Offbib5EUXFS1kkz2u/tAhuP32I8Pd3LmHg90LX1jUNXfu5NYlqf91\nLcxFxKXA+yhmv/xIZr6r4fZZwCeAi4DtwOsyc1N52zuA3wIOAr+Xmdc3OX4+8kgyb147pUuFzGJ8\n5cyZxVp0Tpgj9bfMojVk/3544onivP7yE08Ut0PxeW88jbZ9rNvnzIETTyz+KdTrv0eOFc6aXd6+\nvXheJ50ES5cW57XLS5YUAeWxx2DPnqNPo20fGWke8ubOLR7rsceODGV79hRLzCxaBAsXHnk+1uXj\njqv6FR9dJqxZczjc3XADbNtWBOSlSw+faq93s9OSJTBrVtXPRFLVuhLmImIacA/wMuB+4Ebgisy8\nq26f/wA8OzOvjojXAa/KzCsi4hzgU8DPAMuBfwKelg0P2jibpXrP8PAwQ0NDVZcxqj174PnPL/4Y\nLloE8+bB/PnFeSun+fOL/z7P6OmVF4+t19+jQTfV358DB+DBB4tZA2unbduK8x07jgxizcJZ47aI\nouVn9uzic11/Pnt20VKT2fwE47tt165h9u0b4uDBItQ1Oy1cOPb20YJH5uFw9OijR54329bs/NFH\ni9fzWOGs2eWTTup8WDhwoHnI27On6BZ5wglHhrL58yc++3C/fI5q7/VDDx15qoXsxtP27cXr1Szo\nnXRS8TpCEaAPHRr9vJ3b4PDnqROnGTPgu9/tnffn0CHYtw8ef/zo80OHjv79Un991qypO1N2v3yG\nBtV4wlwrX10vBtZm5sbyQVYDlwF31e1zGVCbU/BzwF+Wl18JrM7Mg8B9EbG2PN4P2ylS1ev1D//c\nucX4uTvuOPyFp/60ezds3tz8ttrpsceKX+SNQW/x4ub/Va1dX7y4N0Jgr79HnTAyUryXta5XO3cW\n1w8cKLpwjYwcfd5s21i3RRz+Erp4cfNTu1+M+/X9OXjwyJBWC2jNAtvSpXDqqUV3+dNOKy4///nF\n69jsi1OzkFa7PH365D/XVauGWbVqiP37i5+rXbuKn7Fdu44+bdzYfPvOncXPTy3Y1Vq8Hn20CDjH\nHVd8aZ8379jnJ55YTNrR7LZFi7oTzto1c+bh5zlZ+uVzFFG8Vyec0Np47kOHip+dZmHvzjsP/1xN\nn16EjNHOj3XbjBnFz0ztOhT/ONm3r/jZrf1DZbynQ4dg2rRhjj9+iFmzip+PmTN58vJ4t8HRgWy0\nkFZ/fvBg8XmbM+fo84ij/5HU+A+mmTNH//3U7Hr994D6HgATOZ8+vThuK6dW9129epjt24ee/BtY\nC/fjvXzoUPE61LfKN7bSN16vvQfqjFa+gi4DNtdd30IRyJruk5kjEbE7IhaV279ft9/WcpvUcQsW\nwM/+7PjvXxtk/8gjR4a8HTsO/1G9554iNNb/wd25s/ji36wLTbMAuHDh4V9iIyNFEKmFkdrl+lMr\n2w8eLMZwfP7zR/7Snjlz9F/oo91Wv71Z97SJyixe4/pxMrVT/fVmlx97rPhCW99Fa8GCw19Qan/Q\n6i83O6+FhdH2ySwe7+GHYe3a4vzhh4s6auezZx877C1efOTte/YUoefAgeKLQu29q11utm2sywcP\nHt3aNNHLe/ceGdgefrj4ua2Fs1pQu/jiI0Pb0qXVBLBumD378Ge2XZnFl8lauKuNRZs3r/giM1Ve\nI3XWtGmHf1ecfXbV1YzPyAj80R/Bf/kv7f2eG2tbZvNA1njeuG3WrPH/3cosahgt7DULgrXWzvrf\nqxM9P3So+D1/rNPjjx95vfZPyWanAwfgrruKfWpBvz70t3u51oK5f3/xT79aq3xjK33j9f37D4e8\n0cJffaivP422vZXTtGmHv8/UX64/tbv9rLOqD6bdak8wb6vvRBz+ZXLqqa3fb2Sk+MLbrCvNLbcc\n3bVmz57il8KBA8X9a79kakGq2Wm02+rD15o18OlPH/lL+1i/0MfaXvsj2tgDerRfaK384sssAvKc\nOYfDWH0wq10+88zmty1Y0BtfhmvPoxbyGk/r1hXjZuq3PfAAfPKTrf93upXLtQkoGseBTeTycccd\nHdJ6oeW5X9TG3s2Z097vEanfTZ9e/F5asKDqSiauvpv3VJvPYdWq4lSlkZHDIW+08Fcf6Jv9Q/Px\nx5v/o/tYp0OHjuxa33h9PNvvvLP6XhKtjJl7PrAqMy8tr78dyPpJUCLi6+U+P4yI6cC2zFzauG9E\nfAO4JjN/2PAYDpiTJEmSNNC6MWbuRuCsiDgD2AZcAVzZsM8/AldRjIW7HPh2uf3LwKci4r0U3SvP\nAm6YaNGSJEmSNOjGDHPlGLi3ANdzeGmCNRFxLXBjZn4F+Ajwd+UEJw9TBD4y886I+CxwJ3AAuNpp\nKyVJkiRp4npi0XBJkiRJUnsqX0UjIi6NiLsi4p6IeFvV9ehoEXFfRNwaET+OiKO6yWryRcRHIuLB\niLitbtvCiLg+Iu6OiG9GxBQYht6fRnl/romILRFxc3m6tMoaB1lELI+Ib0fEHRHxk4j43XK7n6Ee\n0eQ9+o/ldj9HPSIiZkfED8vvBj+JiGvK7WdGxA/K73WfiQinUKrAMd6fj0XE+nL7zRFxXtW1DrqI\nmFa+F18ur7f1Gao0zJULkn8QeDlwLnBlRPTppLxT2iFgKDMvyMzGZSlUjY9RfG7qvR34p8x8BsW4\n1XdMelWqafb+ALwnMy8sT9+Y7KL0pIPAH2TmucALgN8p//b4Geodje/RW+q+H/g56gGZuR94SWZe\nAJwP/EJEPA94F/AXmfl0YBfwpgrLHFjHeH8A3lp+p7swM28b/SiaJL9HMSStpq3PUNUtc08uSJ6Z\nB4DaguTqLUH1Pyuqk5n/Auxs2HwZcF15+TrgVya1KD1plPcHXLalJ2TmA5l5S3n5MWANsBw/Qz1j\nlPeotk6tn6MekZl7y4uzKeZhSOAlwOf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11d4678990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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585k/f/6wz1OVzdAj4lbg/0sp3V+5fwzwXeBU8nTMW4AjyNNDnwBageXAPcBl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\nkiSpRP5/lAdNmH4tEtkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11d509ed10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frame = audio[6*44100 : 6*44100 + 1024]\n",
    "spec = spectrum(w(frame))\n",
    "mfcc_bands, mfcc_coeffs = mfcc(spec)\n",
    "\n",
    "plot(spec)\n",
    "plt.title(\"The spectrum of a frame:\")\n",
    "show() # unnecessary if you started \"ipython --pylab\"\n",
    "\n",
    "plot(mfcc_bands)\n",
    "plt.title(\"Mel band spectral energies of a frame:\")\n",
    "show() # unnecessary if you started \"ipython --pylab\"\n",
    "\n",
    "plot(mfcc_coeffs)\n",
    "plt.title(\"First 13 MFCCs of a frame:\")\n",
    "show() # unnecessary if you started \"ipython --pylab\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computations on frames\n",
    "Now let's compute the mel band energies and MFCCs in all frames."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The way we would do it in Matlab is by slicing the frames manually (the first frame starts at moment 0, i.e., with the first audio sample):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mfccs = []\n",
    "melbands = []\n",
    "frameSize = 1024\n",
    "hopSize = 512\n",
    "\n",
    "for fstart in range(0, len(audio)-frameSize, hopSize):\n",
    "    frame = audio[fstart:fstart+frameSize]\n",
    "    mfcc_bands, mfcc_coeffs = mfcc(spectrum(w(frame)))\n",
    "    mfccs.append(mfcc_coeffs)\n",
    "    melbands.append(mfcc_bands)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is ok, but there is a much nicer way of computing frames in Essentia by using *FrameGenerator*, the [FrameCutter](http://essentia.upf.edu/documentation/reference/std_FrameCutter.html) algorithm wrapped into a python generator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kSZK0uEwbkKWUrgY295l+I/CkuaiUJEmSJC0GI/2ohyRJkiRp/AzIJEmSJKkSAzJJkiRJ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\n1uZJuqvYxqpcTq3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11d4fb1710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AYA9U+naYd3VrPsXoP/hEUpZFH3Bl39CwmwQjjJAAYBkDJBgBYIZKZqkAIMEI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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11d413fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mfccs = []\n",
    "melbands = []\n",
    "\n",
    "for frame in FrameGenerator(audio, frameSize=1024, hopSize=512, startFromZero=True):\n",
    "    mfcc_bands, mfcc_coeffs = mfcc(spectrum(w(frame)))\n",
    "    mfccs.append(mfcc_coeffs)\n",
    "    melbands.append(mfcc_bands)\n",
    "\n",
    "# transpose to have it in a better shape\n",
    "# we need to convert the list to an essentia.array first (== numpy.array of floats)\n",
    "mfccs = essentia.array(mfccs).T\n",
    "melbands = essentia.array(melbands).T\n",
    "\n",
    "# and plot\n",
    "imshow(melbands[:,:], aspect = 'auto', origin='lower', interpolation='none')\n",
    "plt.title(\"Mel band spectral energies in frames\")\n",
    "show() # unnecessary if you started \"ipython --pylab\"\n",
    "\n",
    "imshow(mfccs[1:,:], aspect='auto', origin='lower', interpolation='none')\n",
    "plt.title(\"MFCCs in frames\")\n",
    "show() # unnecessary if you started \"ipython --pylab\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can configure frame and hop size of the frame generator, and whether to start the first frame or to center it at zero position in time. For the complete list of available parameters see the documentation for the [FrameCutter](http://essentia.upf.edu/documentation/reference/std_FrameCutter.html).\n",
    "\n",
    "Note, that when plotting MFCCs, we ignored the first coefficient to disregard the power of the signal and only plot its spectral shape.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Storing results to Pool\n",
    "A **Pool** is a container similar to a C++ map or Python dict which can contain any type of values (easy in Python, not as much in C++...). Values are stored in there using a name which represent the full path to these values; dot ('.') characters are used as separators. You can think of it as a directory tree, or as namespace(s) + local name.\n",
    "\n",
    "Examples of valid names are: ``\"bpm\"``, ``\"lowlevel.mfcc\"``, ``\"highlevel.genre.rock.probability\"``, etc...\n",
    "\n",
    "Let's redo the previous computations using a pool. The pool has the nice advantage that the data you get out of it is already in an ``essentia.array`` format (which is equal to numpy.array of floats), so you can call transpose (``.T``) directly on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kSZK0uEwbkKWUrgY295l+I/CkuaiUJEmSJC0GI/2ohyRJkiRp/AzIJEmSJKkSAzJJkiRJ\nqsSATJIkSZIqMSCTJEmSpEoMyCRJkiSpEgMySZIkSarEgEySJEmSKjEgk6RZmcgvSZKkmTMgkyRJ\nkqRKDMgkSZIkqRIDMkmSJEmqxIBMkiRJkioxIJMkSZKkSgzIJEmSJKkSAzJJkiRJqsSATJIkVWY+\nP0mLlwGZJEmSJFViQCZJkiRJlRiQSZIkSVIlBmSSJEmSVIkBmSRJkiRVYkAmSZIkSZUYkEmSJElS\nJQZkkiRJklSJAZkwGaekhcv7m6TFwgTr85UBmSRJkiRVYkAmSZIkSZUYkEmSJElSJQZkkiRJklSJ\nAZkkSZIkVWJAJkmSJEmVGJBJkiRJUiXTBmQRcXxEXBoR/xoRV0fEb+fp50XErRFxRX6dPffV1fhN\nAHtqV0KS5oj3N0mLQft5znvefDRK9rg9wOtTSldFxBrgWxHxpTzvXSmld81d9SRJkiRp4Zo2IEsp\n3Q7cnod3RsS1wHF5dsxh3SRJkiRpQZvR/5BFxCbgVODyPOk1EXFVRHwwIg4bc90kSZIkaUGLlNJo\nCzZfV9wC/ElK6eKIOAr4SUopRcR/AY5JKb2iz3oJziimbMovHRr8HzJpdtovGngdSQfO60iaHT/P\nHXpuyi9Ytmwpk5OXklLq++3CUf6HjIiYAD4FfDildDFASunHxSJ/BXx2cAlnjrIZSZIkSVoANtE+\nhFqxYjmTk5cOXHLUryx+CPhOSuk97YSIOLqY/zzgmhnWUpIkSZIWtWmfkEXEU4CXAFdHxJVAAt4E\nvDgiTgX20TyPe9Uc1lOSJEmSFpxRfmXxa8DSPrM+P/7qSJIkSdLiMdL/kGkh8x9ApdnxGpJmz+tI\nmh2voflsRj97L0mSJEkaHwMySZIkSarEgEySJEmSKjEgkyRJkqRKDMgkSZIkqRIDMkmSJEmqxIBM\nkiRJkioxIJMkzXOm1JQkzV8GZJIkSZJUiQGZJEmSJFViQCZJkiRJlRiQSZIkSVIlBmSSJEmSVIkB\nmSRJkiRVYkAmSZIkSZUYkEmSJEnSAZtgNjkxDcgkSZIkqRIDMkmSJEmqxIBMkiRJkioxIJMkSZKk\nSgzIJEmSJKkSAzJJkiRJqsSATJIkSZIqMSCTJEmSpEoMyCRJ89ye2hXQ2Bx4YlVJqmcPs+mLDMgk\nSZIkqRIDMkmSJEmqxIBMkiRJkioxIJMkSZKkSgzIJEmSJKkSAzJJkiRJqmTagCwijo+ISyPiXyPi\n6oh4XZ6+PiK+GBHXRcQXIuKwua+uJEmSJC0cozwh2wO8PqX0WODJwGsi4mTgjcCXU0onAZcCfzB3\n1ZQkLU4TmJtqMTGnnKRD1dz1R9MGZCml21NKV+XhncC1wPHAOcCFebELgefOSQ0lSZIkaYGa0f+Q\nRcQm4FTg68DGlNI2aII2YMO4KydJkiRJC9nIAVlErAE+BfxOflKWOot0xyVJkiRJQ4z0RciImKAJ\nxj6cUro4T94WERtTStsi4mjgjsElbCmGN+WXJEmSJC1EN+UX7N69dOiSo/5n2oeA76SU3lNM+wzw\nMuDtwLnAxX3Wy84ccTOSJEmSNN9ton0ItWLFciYnLx245LQBWUQ8BXgJcHVEXEnz1cQ30QRin4iI\nlwM3Ay+YZa0lSZIkaVGZNiBLKX0NGPSc7WnjrY4kSZIkLR4z+pVFSVKXebIkSZq/6vfjBmSSJEmS\nVIkBmSRJkiRVYkAmSZIkSZUYkEmSJElSJQZkkiRJklSJAZkkSZIkVWJAJkmSJEmVGJBJkiRJUiVm\nM5WkWdlTuwLzUNn1THf82vkTIywrSdKo2r5olL5lbvsgn5BJkiRJUiUGZJIkSZJUiQGZJEmSJFVi\nQCZJkiRJlRiQSZIkSVIlBmSSJEmSVIkBmSRJkiRVYkAmSTrI4gDWMQeZJGmc9nCopGQ2IJMkSZKk\nSgzIJEmSJKkSAzJJkiRJqsSATJIkSZIqMSCTJEmSpEoMyCRJkiSpEgMySZIkSarEgEyS5o0JDpWc\nKZIkzW8TjJ7jcm5zYRqQSZIkSVIlBmSSJEmSVIkBmSRJkiRVYkAmSZIkSZUYkEmSJElSJQZkkiRJ\nklSJAZkkSZIkVTJtQBYR50fEtoj4djHtvIi4NSKuyK+z57aakiRJkrTwjPKE7ALgmX2mvyultDm/\nPj/mekmS9hP5Nd+TQ0/SJNncw/zfF0labCY6r/mq7Ifa/ei3T3O/j9MGZCmlrwJ39ZkV46+OJEmS\nJC0es/kfstdExFUR8cGIOGxsNZIkSZKkReJAA7K/BE5MKZ0K3A68a3xVkiRJkqTF4YC+FJlS+nEx\n+lfAZ4evsaUY3pRfkiRJkrQQ3ZRfsHv30qFLjhqQtf9J3oxEHJ1Suj2PPg+4ZvjqZ464GUmSJEma\n7zbRPoRasWI5k5OXDlxy2oAsIj5CE1EdGRE/BM4DnhoRpwL7aEK/V82uwpIkSZK0+EwbkKWUXtxn\n8gVzUBdJkiRJWlRm8yuL0jzTzTMxzjLHlY9jLuqohWOSXg6vWiYGDI+jvHFY2Rk/YczlS+Mwl9fR\nXJRnf6TSns6rhmF5w2ZaRqnfPs10H1eyf180vI4GZJIkSZJUiQGZJEmSJFViQCZJkiRJlRiQSZIk\nSVIlBmSSJEmSVIkBmSRJkiRVYkAmSZIkSZUYkEmSJElSJQZkWkTaZH/rGV+Sy8ivIxhPgsS2jA1j\nKEsLV7+E5AcrcWvbPg+0jXaTZcJ467+xU979YypXGqdlNH3ResZzry+vy3Fo+8px9pdaWGr2QzD1\n89xMt9uu01577WevQQnWZ1r+svxaW0yLoWsYkEmSJElSJQZkkiRJklSJAZkkSZIkVWJAJkmSJEmV\nGJBJkiRJUiUGZJIkSZJUiQGZJEmSJFVicol5ocyVMB8davXfRZMfYtcYympzKt1D73Kazb625d3F\n1HxN46jrYtM9H4daO5yNZfT2r2wbB2MfV9K0z3Z4pm2zW7/2eoT9u6Tusv2usbXAZFGP22jyAgLc\nkV86MOO4px1qDpX7wATjva+X1+Uqmj5pttr6tTmVxlHmYrQQryPo5dVaCuwthse1n9Mdt/YaWlYs\nN9ttDwqLprtvrKWXc+w2etfKCcXw5NAt+4RMkiRJkioxIJMkSZKkSgzIJEmSJKkSAzJJkiRJqsSA\nTJIkSZIqMSCTJEmSpEoMyCRJkiSpkoOUh2zUzZT5gsppw8a769dIrTbdNru5C0bJk3AyTS6R1o78\nfsOoldJ+2hwR9wwYn6mVnXXbvGEHmgejbEeTebwt0zxkMxfASfTO0Ubg+3n4rr5rHNrKPCj3V6wD\nTM2nMup1NCynTOozr999dUN+v62Ytho4CrimT92GlTVM2Zf065e6y466jVHKmm0fNmz9YfemfvOO\ny+9HFNPuB64foTwNtpap10t3fCbKdlq2r9nmvurW6QTMQ3agHpHfJ+l9rrsHuLlOdcZmsvPeHZ5r\n3Xx7Kxm9vQ9ablAus+5nsBOYev52MfU+2bqLUe/pPiGTJEmSpEoMyCRJkiSpEgMySZIkSarEgEyS\nJEmSKjEgkyRJkqRKDMgkSZIkqRIDMkmSJEmqZNqALCLOj4htEfHtYtr6iPhiRFwXEV+IiMPmtpqS\nJEmStPBESmn4AhG/AOwELkopnZKnvR24M6X0joj4fWB9SumNA9ZPcN6Yq70YLKN/YseDmXRvXMqE\ntrWsZP/Efsvy+9I+86azluZc9FtvPfsnHh4lSWe/5KCzTV69mK1l6vmZoF5C5bm0ss+0MkHsnj7T\nD1S/NtpeR2ma8rvXwPr83r1W2vJW0lxj7fIBnJaHt9FLyrmMJkHofUX5bRnz8X55KFk2YPp8Pq79\n+oKDZaJ479bhQO/1J+b3GzrT2/tCv0S37bx+x2HQvH79mqY3Qe866p6H+XwdDdImZ+6XDLm7/9P1\nR4Pa4rBrZVjbbnX7xbKu5fRlRXkTeXvt/BOBO/Lw/TT9U6v8TLkMWAPAmjX3snPnH5JSKhd+wLRP\nyFJKX2X/q/Ac4MI8fCHw3OnKkSRJkiRNdaD/Q7YhpbQNIKV0O7BhfFWSJEmSpMVhXD/qMfx7j5Ik\nSZKk/fT7kucotkXExpTStog4mt4XKQfYUgxvyi9JkiRJWoh+AGwFYPfu4f8zOGpAFkz9j7XPAC8D\n3g6cC1w8fPUzR9yMJEmSJM13DwdOAWDFinuZnLx04JKj/Oz9R4B/Bh4VET+MiN8E3gY8PSKuA87K\n45IkSZKkGZj2CVlK6cUDZj1tzHWRJEmSpEVlXD/qobGbpMltcH8ebl/z0YH+q+Jcm80x3Qhsptm3\n7v6dQvPguLQ2v/odi5X51S+nxqr80sxNMPXaWWg5yNq2F8VrWZ9Xudwo12K/Nt22335ttD2+05Xd\nzYd0RH6d3FluTX6tprmWym/MX51fjy6WT8COoo5lnVaOUC8NNjngNZ8Nyq12MLT38345knblV7+8\ngoOsAx6ZX+s781bn128PqcdM2A8duH6f5eb7ddRV9jOrmNontcMT9O9fBmn7sO522mtluvqMahlw\nen5113tEfp3J1Pr/kN51tIGp57Wsczs+/X3HgEySJEmSKjEgkyRJkqRKDMgkSZIkqRIDMkmSJEmq\nxIBMkiRJkioxIJMkSZKkSgzIJEmSJKkSE7QsehNMzQ00F/bSa2pzva2uNqfLsJwVu0ZcDnr7cT2w\nnSYXGcA3imX+GXgFcG0evw3YmYdnuv9tvqUyh1aZv2QZU/NedHNttdtbSbNv/c7DsDZwMNrHXJmv\n9R5Vm5ur3M89NDm8dvWZlxjtmOyhaS/lsqPkzJnJdQTNdQFwQn4B3FysuzNPX1rUq23fn6eXc6yd\n1uZJuqvYxqpcTq3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11d425e310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AYA9U+naYd3VrPsXoP/hEUpZFH3Bl39CwmwQjjJAAYBkDJBgBYIZKZqkAIMEI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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11d44c4ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pool = essentia.Pool()\n",
    "\n",
    "for frame in FrameGenerator(audio, frameSize = 1024, hopSize = 512, startFromZero=True):\n",
    "    mfcc_bands, mfcc_coeffs = mfcc(spectrum(w(frame)))\n",
    "    pool.add('lowlevel.mfcc', mfcc_coeffs)\n",
    "    pool.add('lowlevel.mfcc_bands', mfcc_bands)\n",
    "\n",
    "imshow(pool['lowlevel.mfcc_bands'].T, aspect = 'auto', origin='lower', interpolation='none')\n",
    "plt.title(\"Mel band spectral energies in frames\")\n",
    "show() # unnecessary if you started \"ipython --pylab\"\n",
    "\n",
    "imshow(pool['lowlevel.mfcc'].T[1:,:], aspect='auto', origin='lower', interpolation='none')\n",
    "plt.title(\"MFCCs in frames\")\n",
    "show() # unnecessary if you started \"ipython --pylab\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregation and file output\n",
    "As we are using such a nice language as Python, we could use its facilities for writing data to a file, but for the sake of this tutorial let's do it using the ``YamlOutput`` algorithm, which writes a pool in a file using the [YAML](http://yaml.org/) or [JSON](http://en.wikipedia.org/wiki/JSON) format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "output = YamlOutput(filename = 'mfcc.sig') # use \"format = 'json'\" for JSON output\n",
    "output(pool)\n",
    "\n",
    "# or as a one-liner:\n",
    "YamlOutput(filename = 'mfcc.sig')(pool)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can take a while as we actually write the MFCCs for all the frames, which\n",
    "can be quite heavy depending on the duration of your audio file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's assume we do not want all the frames but only the mean and variance of those frames. We can do this using the [PoolAggregator](http://essentia.upf.edu/documentation/reference/std_PoolAggregator.html) algorithm on our pool with frame value to get a new pool with the aggregated descriptors (check the documentation for this algorithm to get an idea of other statistics it can compute):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original pool descriptor names:\n",
      "['lowlevel.mfcc', 'lowlevel.mfcc_bands']\n",
      "\n",
      "Aggregated pool descriptor names:\n",
      "['lowlevel.mfcc.mean', 'lowlevel.mfcc.var', 'lowlevel.mfcc_bands.mean', 'lowlevel.mfcc_bands.var']\n"
     ]
    }
   ],
   "source": [
    "# compute mean and variance of the frames\n",
    "aggrPool = PoolAggregator(defaultStats = [ 'mean', 'var' ])(pool)\n",
    "\n",
    "print 'Original pool descriptor names:'\n",
    "print pool.descriptorNames()\n",
    "print\n",
    "print 'Aggregated pool descriptor names:'\n",
    "print aggrPool.descriptorNames()\n",
    "\n",
    "# and ouput those results in a file\n",
    "YamlOutput(filename = 'mfccaggr.sig')(aggrPool)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how the file with aggregated descriptors looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "metadata:\r\n",
      "    version:\r\n",
      "        essentia: \"2.1-dev\"\r\n",
      "\r\n",
      "lowlevel:\r\n",
      "    mfcc:\r\n",
      "        mean: [-770.771728516, 246.557647705, 53.5677185059, 1.70909059048, -35.5930786133, -27.0709495544, -12.4148387909, -19.2304668427, -33.986038208, -23.4126434326, -15.8186225891, -5.1132478714, -2.86430335045]\r\n",
      "        var: [9531.9296875, 2612.62597656, 1268.72875977, 442.906768799, 258.520568848, 229.063858032, 168.463638306, 126.90486145, 172.914840698, 142.858963013, 209.542709351, 237.36315918, 588.467102051]\r\n",
      "    mfcc_bands:\r\n",
      "        mean: [3.09789697894e-06, 0.0018204189837, 0.00687531381845, 0.00559488125145, 0.00746234040707, 0.00762519706041, 0.00263760378584, 0.00176807912067, 0.00187411252409, 0.0010101441294, 0.000384628627216, 8.97606587387e-05, 0.000103173675598, 0.000462994532427, 0.000481149676489, 0.000150407780893, 4.3479638407e-05, 9.8532436823e-06, 3.4149172734e-06, 4.67248537461e-06, 3.91657658838e-06, 1.8994775246e-06, 2.5756589821e-06, 1.52094037276e-06, 1.15387149435e-06, 3.3445369354e-06, 1.65835001553e-06, 2.04684874916e-06, 1.96311066247e-06, 1.5418397652e-06, 1.18413072414e-06, 1.06164293356e-06, 5.61618151096e-07, 5.55542726488e-07, 1.16678609174e-06, 9.67434175436e-07, 5.79169636694e-07, 4.31736594919e-07, 3.07267100652e-07, 1.74535870201e-07]\r\n",
      "        var: [5.49797274374e-09, 2.71185967904e-06, 3.54826916009e-05, 1.4284183635e-05, 5.45716975466e-05, 5.73034012632e-05, 1.09859265649e-05, 5.82664006288e-06, 6.33308718534e-06, 2.67984637503e-06, 9.67446794675e-07, 1.09977271734e-07, 4.5999644982e-08, 1.01382534012e-06, 2.36311120716e-06, 1.19640631624e-07, 2.97720443854e-08, 1.96152050158e-09, 4.33603347672e-10, 2.12821246737e-10, 1.17798229504e-10, 3.65933568169e-11, 6.12296602309e-11, 2.86074445383e-11, 1.31610087412e-11, 1.15442384818e-10, 6.51916090555e-11, 8.07163641481e-11, 7.98162577698e-11, 4.8446673756e-11, 2.83182435834e-11, 3.88169184296e-11, 9.29356158003e-12, 1.18757833428e-11, 4.44560638302e-11, 3.39185728115e-11, 8.12059273991e-12, 6.04115542313e-12, 3.49459740659e-12, 1.1808879612e-12]\r\n"
     ]
    }
   ],
   "source": [
    "!cat mfccaggr.sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary and more examples\n",
    "There is not much more to know for using Essentia in standard mode in Python, the basics are:\n",
    "\n",
    "* instantiate and configure algorithms\n",
    "* use them to compute some results\n",
    "* and that's pretty much it!\n",
    "\n",
    "You can find a number of python examples in the ```src/examples/tutorial``` folder in the code, including:\n",
    "\n",
    "* computing spectral centroid ([example_spectralcentroid.py](https://github.com/MTG/essentia/blob/master/src/examples/tutorial/example_spectralcentroid.py))\n",
    "* onset detection ([example_onsetdetection.py](https://github.com/MTG/essentia/blob/master/src/examples/tutorial/example_onsetdetection.py))\n",
    "* predominant melody detection ([example_predominantmelody.py](https://github.com/MTG/essentia/blob/master/src/examples/tutorial/example_predominantmelody.py) and [example_predominantmelody_by_steps.py](https://github.com/MTG/essentia/blob/master/src/examples/tutorial/example_predominantmelody_by_steps.py))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Essentia in streaming mode\n",
    "\n",
    "In this section we will consider how to use Essentia in streaming mode.\n",
    "\n",
    "The main difference between standard and streaming is that the standard mode is imperative while the streaming mode is declarative. That means that in standard mode, you tell exactly the computer what to do, whereas in the streaming mode, you \"declare\"  what is needed to be done, and you let the computer do it itself. One big advantage of the streaming mode is that the memory consumption is greatly reduced, as you don't need to load the entire audio in memory. Let's have a look at it.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As usual, first import the essentia module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import essentia\n",
    "from essentia.streaming import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate our algorithms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loader = MonoLoader(filename = '../../../test/audio/recorded/dubstep.wav')\n",
    "frameCutter = FrameCutter(frameSize = 1024, hopSize = 512)\n",
    "w = Windowing(type = 'hann')\n",
    "spec = Spectrum()\n",
    "mfcc = MFCC()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In streaming, instead of calling algorithms like functions, we need to connect their inputs and outputs. This is done using the >> operator.\n",
    "\n",
    "For example, the graph we want to connect looks like this:\n",
    "\n",
    "```\n",
    "----------      ------------      -----------      --------------      --------------\n",
    "MonoLoader      FrameCutter       Windowing        Spectrum            MFCC\n",
    "     audio ---> signal frame ---> frame frame ---> frame spectrum ---> spectrum bands ---> ???\n",
    "                                                                                mfcc  ---> ???\n",
    "----------      ------------      -----------      --------------      --------------\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<essentia.streaming._StreamConnector instance at 0x7f11d42e7710>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loader.audio >> frameCutter.signal\n",
    "frameCutter.frame >> w.frame >> spec.frame\n",
    "spec.spectrum >> mfcc.spectrum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When building a network, all inputs need to be connected, no matter what, otherwise the network cannot be started and we get an error message:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "MFCC::bands is not connected to any sink...",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-5a68facf7b1d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0messentia\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloader\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/essentia/__init__.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(gen)\u001b[0m\n\u001b[0;32m    146\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mVectorInput\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mgen\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnections\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    147\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mEssentiaError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'VectorInput is not connected to anything...'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 148\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_essentia\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    149\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    150\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mEPython\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Successfully imported essentia python module (log fully available and synchronized with the C++ one)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: MFCC::bands is not connected to any sink..."
     ]
    }
   ],
   "source": [
    "essentia.run(loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our case, the outputs of the MFCC algorithm were not connected anywhere. Let's store *mfcc* values in the pool and ignore *bands* values.\n",
    "\n",
    "```\n",
    "----------      ------------      -----------      --------------      --------------\n",
    "MonoLoader      FrameCutter       Windowing        Spectrum            MFCC\n",
    "     audio ---> signal frame ---> frame frame ---> frame spectrum ---> spectrum bands ---> NOWHERE\n",
    "                                                                                mfcc  ---> Pool: lowlevel.mfcc\n",
    "----------      ------------      -----------      --------------      --------------\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pool contains 592 frames of MFCCs\n"
     ]
    }
   ],
   "source": [
    "pool = essentia.Pool()\n",
    "\n",
    "mfcc.bands >> None\n",
    "mfcc.mfcc >> (pool, 'lowlevel.mfcc')\n",
    "\n",
    "essentia.run(loader)\n",
    "\n",
    "print 'Pool contains %d frames of MFCCs' % len(pool['lowlevel.mfcc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's try writing directly to a text file, no pool and no yaml files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first need to disconnect the old connection to the pool to avoid putting the same data in there again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mfcc.mfcc.disconnect((pool, 'lowlevel.mfcc'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a FileOutput and connect it. It is a special connection that has no input, because it can actually take any type of input (the other algorithms will complain if you try to connect an output to an input of a different type)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<essentia.streaming.StreamingAlgo at 0x7f92b7096f50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fileout = FileOutput(filename = 'mfccframes.txt')\n",
    "mfcc.mfcc >> fileout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reset the network otherwise the loader in particular will not do anything useful, and rerun the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "essentia.reset(loader)\n",
    "essentia.run(loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the resulting file (the first 10 lines correspond to the first 10 frames):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-430.671, 87.7917, -10.1204, -50.172, -17.9259, -36.4849, -17.5709, -5.72504, -16.6404, 8.64975, -7.41039, 5.7051, 7.18055]\r\n",
      "[-490.824, 101.549, 68.3375, 10.5324, 9.86464, -21.2722, -12.467, -11.8749, -24.2667, -8.02748, -26.5459, -25.3716, -31.5997]\r\n",
      "[-515.915, 90.4185, 54.5073, 25.2965, 18.2453, 1.56025, 10.0262, 21.2547, 2.83289, 7.16083, -25.8393, -22.4263, -29.8229]\r\n",
      "[-526.075, 76.321, 33.0371, 15.6267, 16.1482, 1.94901, 26.5443, 40.805, 20.866, 20.7323, -16.962, -23.6936, -39.9292]\r\n",
      "[-530.409, 62.8531, 17.8901, 17.2312, 19.4443, 6.44692, 35.9218, 37.0124, 9.91326, 30.9235, -10.691, -12.6595, -30.0003]\r\n",
      "[-532.03, 66.9765, 15.174, 4.41039, 6.51187, 18.4618, 41.4819, 30.0178, 13.5438, 19.5735, -19.7553, -2.62841, -12.9201]\r\n",
      "[-523.106, 85.9242, 15.2094, 11.4087, 9.95426, 19.4773, 20.8585, 27.0054, 19.3617, 19.016, -13.5927, -3.25358, -11.339]\r\n",
      "[-532.996, 90.4333, 13.19, 8.79797, 20.2316, 15.791, 23.7306, 34.2449, 11.5618, 20.3763, -18.6916, -10.9794, -20.2573]\r\n",
      "[-539.285, 74.0864, 20.9641, 18.1156, 11.1981, 6.7221, 25.9186, 38.2328, 8.60174, 16.578, -22.699, -19.8375, -27.6012]\r\n",
      "[-512.555, 60.0025, 25.2892, 3.13255, 18.0855, -2.79686, 22.4047, 25.8552, 6.91858, 11.1513, -10.3943, -17.6128, -8.85415]\r\n"
     ]
    }
   ],
   "source": [
    "!head mfccframes.txt -n 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examples\n",
    "\n",
    "* extracting key by steps ([example_key_by_steps_streaming.py](https://github.com/MTG/essentia/blob/master/src/examples/tutorial/example_key_by_steps_streaming.py))"
   ]
  }
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