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120 lines
3.9 KiB
120 lines
3.9 KiB
import essentia
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import pandas as pd
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import numpy as np
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from essentia.standard import (MonoLoader, Windowing, Spectrum, MFCC,
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ZeroCrossingRate, SpectralCentroidTime, RollOff, Flux, Envelope,
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FlatnessSFX, LogAttackTime, StrongDecay, FlatnessDB, HFC,
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SpectralComplexity, Energy, FrameGenerator, YamlOutput)
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# Disable annoying info level logging
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essentia.log.infoActive = False
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class bcolors:
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BLUE = '\033[94m'
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GREEN = '\033[92m'
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YELLOW = '\033[93m'
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RED = '\033[91m'
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ENDC = '\033[0m'
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def extractFeatures(audio, outputPath, sampleRate):
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if isinstance(audio, str):
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# Loads the audio file specified
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loader = MonoLoader(filename = audio, 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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env = Envelope(attackTime = 2, releaseTime = 300, sampleRate = sampleRate)
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flat = FlatnessSFX()
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logAtt = LogAttackTime(sampleRate = sampleRate)
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strDec = StrongDecay(sampleRate = sampleRate)
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flatDB = FlatnessDB()
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hfc = HFC(sampleRate = sampleRate)
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spcComp = SpectralComplexity(sampleRate = sampleRate, magnitudeThreshold = 2)
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energy = Energy()
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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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frameEFlatness = flat(env(windowedFrame))
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frameLogAtt = logAtt(env(windowedFrame))[1]
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frameStrDec = strDec(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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frameSEFlatness = flat(env(frameSpectrum))
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frameSLogAtt = logAtt(env(frameSpectrum))[1]
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frameSStrDec = strDec(frameSpectrum)
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frameSFlat = flatDB(frameSpectrum)
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frameHFC = hfc(frameSpectrum)
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frameSComp = spcComp(frameSpectrum)
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# Computes cepstral features
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# Discards the bands
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melBandEnergies, mfcc_coeffs = mfcc(frameSpectrum)
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fHzMod = _4HzModulation(melBandEnergies, energy(frameSpectrum), sampleRate)
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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('Flat', frameEFlatness)
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pool.add('LAtt', frameLogAtt)
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pool.add('SDec', frameStrDec)
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pool.add('SR', frameSR)
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pool.add('SF', frameSF)
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pool.add('SEFlat', frameSEFlatness)
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pool.add('SFlat', frameSFlat)
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pool.add('SLAtt', frameSLogAtt)
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pool.add('SSDec', frameSStrDec)
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pool.add('HFC', frameHFC)
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pool.add('SComp', frameSComp)
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for index, coef in enumerate(mfcc_coeffs):
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pool.add('mfcc' + str(index), coef)
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pool.add('4HzMod', fHzMod)
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YamlOutput(filename = outputPath, format = 'json', writeVersion = False)(pool)
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return pd.DataFrame(np.array([pool[i] for i in pool.descriptorNames()]).T, columns = pool.descriptorNames())
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def _4HzModulation(melEnergies, frameEnergy, sampleRate):
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from scipy.signal import butter, sosfilt, sosfreqz
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nyquist = 0.5 * sampleRate
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lowCut = 3 / nyquist
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highCut = 5 / nyquist
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sos = butter(N = 2, Wn = [lowCut, highCut], analog = False, btype = 'band',
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output = 'sos')
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filtered = sosfilt(sos = sos, x = melEnergies)
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energySum = sum(filtered)
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return energySum / frameEnergy
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# Prints a nice message to let the user know the module was imported
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print(bcolors.BLUE + 'feature_extractor loaded' + bcolors.ENDC)
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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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