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59 lines
1.9 KiB
59 lines
1.9 KiB
import essentia
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import essentia.standard
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from essentia.standard import *
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import essentia.streaming
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from pylab import plot, show, figure, imshow
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import matplotlib.pyplot as plt
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def extractFeatures(audioPath, outputPath, sampleRate):
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# Loads the audio file specified
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loader = essentia.standard.MonoLoader(filename = audioPath, sampleRate = sampleRate)
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audio = loader()
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# Sets up the functions that will be used
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# TODO check if zero phase windowing is something we might want
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window = Windowing(normalized = False, size = 6144, type = 'hamming',
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zeroPhase = False)
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spectrum = Spectrum()
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mfcc = MFCC(inputSize = 6144, sampleRate = sampleRate)
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zcr = ZeroCrossingRate()
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sc = SpectralCentroidTime(sampleRate = sampleRate)
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sr = RollOff(sampleRate = sampleRate)
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sf = Flux()
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# Creates a pool to collect the values of the features
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pool = essentia.Pool()
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# Slices the signal into frames
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for frame in FrameGenerator(audio, frameSize = 6144, hopSize = 3072,
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startFromZero = True , validFrameThresholdRatio = 0.7):
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# Applies a window function to the frame
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windowedFrame = window(frame)
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# Computes time domain features
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frameZCR = zcr(windowedFrame)
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frameSC = sc(windowedFrame)
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# Computes spectral features
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frameSpectrum = spectrum(windowedFrame)
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frameSR = sr(frameSpectrum)
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frameSF = sf(frameSpectrum)
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# Discards the bands
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mfcc_coeffs = mfcc(frameSpectrum)[1]
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# Adds the values to the pool
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pool.add('ZCR', frameZCR)
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pool.add('SC', frameSC)
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pool.add('SR', frameSR)
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pool.add('SF', frameSF)
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pool.add('mfcc', mfcc_coeffs)
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YamlOutput(filename = outputPath, format = 'json', writeVersion = False)(pool)
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# Prints a nice message to let the user know the module was imported
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print('feature_extractor loaded')
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# Enables executing the module as a standalone script
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if __name__ == "__main__":
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import sys
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extractFeatures(sys.argv[1], sys.argv[2], int(sys.argv[3]))
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