{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 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 research 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 or [Jupyter python notebooks](http://jupyter.org/) instead of the standard python interpreter. 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)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using Essentia in standard mode" ] }, { "cell_type": "markdown", "metadata": {}, "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. You can use IPython (start it with the ```--pylab``` option to have interactive plots) or Jupyter notebooks.\n", "\n", "### Exploring the python module" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let’s investigate a bit the Essentia package." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['AfterMaxToBeforeMaxEnergyRatio', 'AllPass', 'AudioLoader', 'AudioOnsetsMarker', 'AudioWriter', 'AutoCorrelation', 'BFCC', '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', 'FreesoundExtractor', 'FrequencyBands', 'GFCC', 'GeometricMean', 'HFC', 'HPCP', 'HarmonicBpm', 'HarmonicMask', 'HarmonicModelAnal', 'HarmonicPeaks', 'HighPass', 'HighResolutionFeatures', 'HprModelAnal', 'HpsModelAnal', 'IDCT', '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', 'MusicExtractor', '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', 'SpectrumCQ', 'SpectrumToCent', 'Spline', 'SprModelAnal', 'SprModelSynth', 'SpsModelAnal', 'SpsModelSynth', 'StartStopSilence', 'StereoDemuxer', 'StereoMuxer', 'StereoTrimmer', 'StochasticModelAnal', 'StochasticModelSynth', 'StrongDecay', 'StrongPeak', 'SuperFluxExtractor', 'SuperFluxNovelty', 'SuperFluxPeaks', 'TCToTotal', 'TempoScaleBands', 'TempoTap', 'TempoTapDegara', 'TempoTapMaxAgreement', 'TempoTapTicks', 'TonalExtractor', 'TonicIndianArtMusic', 'TriangularBands', 'TriangularBarkBands', 'Trimmer', 'Tristimulus', 'TuningFrequency', 'TuningFrequencyExtractor', 'UnaryOperator', 'UnaryOperatorStream', 'Variance', 'Vibrato', 'WarpedAutoCorrelation', 'Windowing', 'YamlInput', 'YamlOutput', 'ZeroCrossingRate', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_c', '_create_essentia_class', '_create_python_algorithms', '_essentia', '_reloadAlgorithms', '_sys', 'algorithmInfo', 'algorithmNames', 'copy', 'essentia', 'iteritems']\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", " " ] }, { "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)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "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", " | dctType:\n", " | integer ∈ [2,3] (default = 2)\n", " | the DCT type\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", " | liftering:\n", " | integer ∈ [0,inf) (default = 0)\n", " | the liftering coefficient. Use '0' to bypass it\n", " | \n", " | logType:\n", " | string ∈ {natural,dbpow,dbamp,log} (default = \"dbamp\")\n", " | logarithmic compression type. Use 'dbpow' if working with power and 'dbamp'\n", " | if working with magnitudes\n", " | \n", " | lowFrequencyBound:\n", " | real ∈ [0,inf) (default = 0)\n", " | the lower bound of the frequency range [Hz]\n", " | \n", " | normalize:\n", " | string ∈ {unit_sum,unit_max} (default = \"unit_sum\")\n", " | 'unit_max' makes the vertex of all the triangles equal to 1, 'unit_sum'\n", " | makes the area of all the triangles equal to 1\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", " | type:\n", " | string ∈ {magnitude,power} (default = \"power\")\n", " | use magnitude or power spectrum\n", " | \n", " | warpingFormula:\n", " | string ∈ {slaneyMel,htkMel} (default = \"slaneyMel\")\n", " | The scale implementation type. use 'htkMel' to emulate its behaviour.\n", " | Default slaneyMel.\n", " | \n", " | weighting:\n", " | string ∈ {warping,linear} (default = \"warping\")\n", " | type of weighting function for determining triangle area\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", " | The parameters of this algorithm can be configured in order to behave like\n", " | HTK [3] as follows:\n", " | - type = 'magnitude' \n", " | - warpingFormula = 'htkMel' \n", " | - weighting = 'linear'\n", " | - highFrequencyBound = 8000\n", " | - numberBands = 26\n", " | - numberCoefficients = 13\n", " | - normalize = 'unit_max'\n", " | - dctType = 3\n", " | - logType = 'log'\n", " | - liftering = 22\n", " | \n", " | In order to completely behave like HTK the audio signal has to be scaled by\n", " | 2^15 before the processing and if the Windowing and FrameCutter algorithms\n", " | are used they should also be configured as follows. \n", " | \n", " | FrameGenerator:\n", " | - frameSize = 1102 \n", " | - hopSize = 441 \n", " | - startFromZero = True \n", " | - validFrameThresholdRatio = 1 \n", " | \n", " | Windowing:\n", " | - type = 'hamming' \n", " | - size = 1102 \n", " | - zeroPadding = 946 \n", " | - normalized = False \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", " | IDCT can be used to compute smoothed Mel Bands. In order to do this: \n", " | - compute MFCC\n", " | - smoothedMelBands = 10^(IDCT(MFCC)/20) \n", " | \n", " | Note: The second step assumes that 'logType' = 'dbamp' was used to compute\n", " | MFCCs, otherwise that formula should be changed in order to be consistent.\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,\n", " | vol. 1, 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", " | [3] Young, S. J., Evermann, G., Gales, M. J. F., Hain, T., Kershaw, D.,\n", " | Liu, X., … Woodland, P. C. (2009). The HTK Book (for HTK Version 3.4).\n", " | Construction, (July 2000), 384, https://doi.org/http://htk.eng.cam.ac.uk\n", " | \n", " | Method resolution order:\n", " | Algo\n", " | Algorithm\n", " | builtins.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", " | __new__(*args, **kwargs) from builtins.type\n", " | Create and return a new object. See help(type) for accurate signature.\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" ] } ], "source": [ "help(essentia.standard.MFCC)" ] }, { "cell_type": "markdown", "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]( )).\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" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "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": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This is how the audio we want to process sounds like\n", "import IPython\n", "IPython.display.Audio('../../../test/audio/recorded/dubstep.wav')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, the MonoLoader will output audio with 44100Hz sample rate 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:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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JU2XYJkvPxhsDZ53lv40pzfKlY9EC+HHaacDDD6veS1v0h3XCBOCRR9Kd3/Yj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"text/plain": [ "" ] }, "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": 7, "metadata": {}, "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. 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": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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BnGRmKyR9TtLPzOytzOZHSnrVzGZLelrSzfnBrpBiA6pEo2Xu2kXlDgAAAADi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\nmoksXSqSkBBa3qZMEUlNdT526RKf/913oZ+1ZAnrmzlz+Fx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IMyJiFnAF8OWGff4RuKq8fDnw7fLyJsrxcxExF3g+cFfbVVas11vmli93zJwk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\nlPeotk6tn6MekZl7y4uzKeZhSOAlwOf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585k/f/6wz1OVzdAj4lbg/0sp3V+5fwzwXeBU8nTMW4AjyNNDnwBageXAPcBl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\nT5IkSZJKxJAnSZIkSSViyJM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"text/plain": [ "" ] }, "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()\n", "\n", "plot(mfcc_bands)\n", "plt.title(\"Mel band spectral energies of a frame:\")\n", "show()\n", "\n", "plot(mfcc_coeffs)\n", "plt.title(\"First 13 MFCCs of a frame:\")\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the case of mel band energies, sometimes you may want to apply log normalization, which can be done using [UnaryOperator](http://essentia.upf.edu/documentation/reference/std_UnaryOperator.html). Using this algorithm we can do different types of normalization on vectors." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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OAv703s8oyeN69IAbb7RF25Kezj0X3n3Xqq1J/IwcCccdZ9OhrrgC/vMfG/nM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\nZZ1+F09DhlgVy0mT0reJdCjdulky98ILidvH+vX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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "logNorm = UnaryOperator(type='log')\n", "plot(logNorm(mfcc_bands))\n", "plt.title(\"Log-normalized mel band spectral energies of a frame:\")\n", "show()" ] }, { "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": 8, "metadata": {}, "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": 11, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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wlO2rhL4jkeBaOkneur70zYufp5GMkZlzvjM31M1RdgKsGouqpFmhS4SRq6Ro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AYA9U+naYd3VrPsXoP/hEUpZFH3Bl39CwmwQjjJAAYBkDJBgBYIZKZqkAIMEI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\nERERkYC8KBuyB9enXoxi5E/IY+uPBV0FeRla3xl0DeTlaPP62aCrIC9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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mfccs = []\n", "melbands = []\n", "melbands_log = []\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", " melbands_log.append(logNorm(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", "melbands_log = essentia.array(melbands_log).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()\n", "\n", "imshow(melbands_log[:,:], aspect = 'auto', origin='lower', interpolation='none')\n", "plt.title(\"Log-normalized mel band spectral energies in frames\")\n", "show()\n", "\n", "imshow(mfccs[1:,:], aspect='auto', origin='lower', interpolation='none')\n", "plt.title(\"MFCCs in frames\")\n", "show()" ] }, { "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 represents 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": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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wlO2rhL4jkeBaOkneur70zYufp5GMkZlzvjM31M1RdgKsGouqpFmhS4SRq6Ro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AYA9U+naYd3VrPsXoP/hEUpZFH3Bl39CwmwQjjJAAYBkDJBgBYIZKZqkAIMEI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\nERERkYC8KBuyB9enXoxi5E/IY+uPBV0FeRla3xl0DeTlaPP62aCrIC9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"text/plain": [ "" ] }, "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", " pool.add('lowlevel.mfcc_bands_log', logNorm(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()\n", "\n", "imshow(pool['lowlevel.mfcc_bands_log'].T, aspect = 'auto', origin='lower', interpolation='none')\n", "plt.title(\"Log-normalized mel band spectral energies in frames\")\n", "show()\n", "\n", "imshow(pool['lowlevel.mfcc'].T[1:,:], aspect='auto', origin='lower', interpolation='none')\n", "plt.title(\"MFCCs in frames\")\n", "show()" ] }, { "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](http://essentia.upf.edu/documentation/reference/std_YamlOutput.html) 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": 11, "metadata": {}, "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 standard deviation 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": 18, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original pool descriptor names:\n", "['lowlevel.mfcc', 'lowlevel.mfcc_bands', 'lowlevel.mfcc_bands_log']\n", "\n", "Aggregated pool descriptor names:\n", "['lowlevel.mfcc.mean', 'lowlevel.mfcc.stdev', 'lowlevel.mfcc_bands.mean', 'lowlevel.mfcc_bands.stdev', 'lowlevel.mfcc_bands_log.mean', 'lowlevel.mfcc_bands_log.stdev']\n", "\n" ] } ], "source": [ "# compute mean and variance of the frames\n", "aggrPool = PoolAggregator(defaultStats = [ 'mean', 'stdev' ])(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", "print('')\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": 17, "metadata": { "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: [-548.465515137, 82.474647522, 23.9920558929, -3.92105984688, 9.37707233429, 0.0550536848605, 7.49981355667, 10.2677164078, 1.46692347527, 8.78695869446, -1.02505850792, 5.55843877792, -4.53198575974]\r\n", " stdev: [92.9804992676, 38.1891860962, 32.8111877441, 25.9349346161, 23.5974235535, 22.8900985718, 23.4571056366, 19.2605209351, 16.999912262, 17.3296813965, 18.9442825317, 18.8146419525, 16.8438053131]\r\n", " mfcc_bands:\r\n", " mean: [0.0430443286896, 0.0152214923874, 0.00659069558606, 0.00539278704673, 0.00387320877053, 0.00304042594507, 0.00343433767557, 0.00261196284555, 0.0023196185939, 0.00208778819069, 0.00157481990755, 0.00235125259496, 0.0010298260022, 0.000758393900469, 0.000447986851213, 0.000321425526636, 0.000237083833781, 0.000205824428122, 0.000197611472686, 0.000161219315487, 0.00013125318219, 0.000133611785714, 0.000161335541634, 0.000107279069198, 0.000109758009785, 0.000123509569676, 8.89756411198e-05, 6.59294382785e-05, 6.32342125755e-05, 5.04779345647e-05, 4.54452601844e-05, 4.45721161668e-05, 4.59850416519e-05, 3.38824975188e-05, 3.47260865965e-05, 4.02627483709e-05, 0.000151775820996, 5.23372145835e-05, 2.56655275734e-05, 2.47844927799e-05]\r\n", " stdev: [0.133796587586, 0.0390107147396, 0.0180664807558, 0.0187281165272, 0.0142051419243, 0.0091609954834, 0.0074247373268, 0.00607190933079, 0.00614743074402, 0.00666699418798, 0.0031948692631, 0.00523599097505, 0.00227958755568, 0.00226213759743, 0.00126964878291, 0.000707814877387, 0.000494667154271, 0.000490796519443, 0.000527630501892, 0.000423052668339, 0.000394845003029, 0.000347238150425, 0.000405938568292, 0.000299160223221, 0.000284110719804, 0.000375441013603, 0.000370208901586, 0.000277761428151, 0.000230042438488, 0.000162357377121, 0.000142114353366, 0.000132423578179, 0.00016263978614, 0.000110406210297, 0.000122218727483, 8.72409000294e-05, 0.000171674750163, 7.6599921158e-05, 4.17021510657e-05, 3.38126337738e-05]\r\n", " mfcc_bands_log:\r\n", " mean: [-6.24955272675, -6.57105016708, -7.34368038177, -7.92592859268, -8.36940193176, -8.14614963531, -7.9449005127, -8.2012386322, -8.46479320526, -8.75048446655, -8.32077884674, -8.22101211548, -8.66969203949, -9.37683773041, -9.66236972809, -9.83232212067, -9.74431324005, -9.8570098877, -10.0467433929, -10.6587133408, -10.9554595947, -10.5900297165, -10.5973072052, -10.9680480957, -11.1300811768, -11.0058526993, -11.3219165802, -11.7418317795, -11.9560842514, -11.8824300766, -11.7772083282, -11.8368644714, -11.8559484482, -12.1547746658, -11.830368042, -11.2242679596, -9.9259595871, -10.9285726547, -11.7089004517, -11.8566360474]\r\n", " stdev: [3.00452470779, 2.58188033104, 2.40453410149, 2.52647328377, 2.71612548828, 2.7103805542, 3.07109212875, 3.06873011589, 2.97640109062, 2.82126951218, 2.66976523399, 2.81773519516, 2.41420173645, 2.39151644707, 2.30262422562, 2.17882299423, 1.98866963387, 2.13317728043, 2.16317820549, 2.34757852554, 2.44092655182, 2.32866716385, 2.44926118851, 2.33606815338, 2.41704487801, 2.46625447273, 2.25014805794, 2.25670146942, 2.5483288765, 2.19981074333, 2.13154554367, 2.15053391457, 2.14865207672, 2.2223200798, 1.91899549961, 1.98338282108, 2.37033653259, 2.3670861721, 2.23138093948, 2.41583800316]\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. Also, streaming mode allows reducing the amount of code which may be very significant for larger projects. 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": 14, "metadata": {}, "outputs": [], "source": [ "import essentia\n", "from essentia.streaming import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate our algorithms:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "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": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "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": 17, "metadata": {}, "outputs": [ { "ename": "RuntimeError", "evalue": "MFCC::bands is not connected to any sink...", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0messentia\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/essentia/__init__.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(gen)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVectorInput\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mEssentiaError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'VectorInput is not connected to anything...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 148\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_essentia\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEPython\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Successfully imported essentia python module (log fully available and synchronized with the C++ one)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;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": 19, "metadata": {}, "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 instead of a pool and 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": 20, "metadata": {}, "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": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ ".StreamingAlgo at 0x7f11815d5d38>" ] }, "execution_count": 21, "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": 22, "metadata": {}, "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": {}, "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))" ] } ], "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }