the-assignment/classifier/feature_extraction/feature_extractor.py

# import essentia.standard
import essentia
from essentia.standard import (MonoLoader, Windowing, Spectrum, MFCC,
	ZeroCrossingRate, SpectralCentroidTime, RollOff, Flux, Envelope,
	FlatnessSFX, LogAttackTime, StrongDecay, FlatnessDB, HFC, LPC,
	SpectralComplexity, FrameGenerator, YamlOutput)

# Disable annoying info level logging
essentia.log.infoActive = False

def extractFeatures(audioPath, outputPath, sampleRate):
	# Loads the audio file specified
	loader = MonoLoader(filename = audioPath, sampleRate = sampleRate)
	audio = loader()

	# Sets up the functions that will be used
	# TODO check if zero phase windowing is something we might want
	window = Windowing(normalized = False, size = 6144, type = 'hamming',
		zeroPhase = False)
	spectrum = Spectrum()
	mfcc = MFCC(inputSize = 6144, sampleRate = sampleRate)
	zcr = ZeroCrossingRate()
	sc = SpectralCentroidTime(sampleRate = sampleRate)
	sr = RollOff(sampleRate = sampleRate)
	sf = Flux()
	env = Envelope(attackTime = 2, releaseTime = 300, sampleRate = sampleRate)
	flat = FlatnessSFX()
	logAtt = LogAttackTime(sampleRate = sampleRate)
	strDec = StrongDecay(sampleRate = sampleRate)
	flatDB = FlatnessDB()
	hfc = HFC(sampleRate = sampleRate)
	lpc = LPC(sampleRate = sampleRate)
	spcComp = SpectralComplexity(sampleRate = sampleRate, magnitudeThreshold = 2)

	# Creates a pool to collect the values of the features
	pool = essentia.Pool()

	# Slices the signal into frames
	for frame in FrameGenerator(audio, frameSize = 6144, hopSize = 3072,
		startFromZero = True , validFrameThresholdRatio = 0.7):
		# Applies a window function to the frame
		windowedFrame = window(frame)

		# Computes time domain features
		frameZCR = zcr(windowedFrame)
		frameSC = sc(windowedFrame)
		frameEFlatness = flat(env(windowedFrame))
		frameLogAtt = logAtt(env(windowedFrame))[1]
		frameStrDec = strDec(windowedFrame)
		frameLPC, frameReflection = lpc(windowedFrame)

		# Computes spectral features
		frameSpectrum = spectrum(windowedFrame)
		frameSR = sr(frameSpectrum)
		frameSF = sf(frameSpectrum)
		frameSEFlatness = flat(env(frameSpectrum))
		frameSLogAtt = logAtt(env(frameSpectrum))[1]
		frameSStrDec = strDec(frameSpectrum)
		frameSFlat = flatDB(frameSpectrum)
		frameHFC = hfc(frameSpectrum)
		frameSComp = spcComp(frameSpectrum)

		# Computes cepstral features
		# Discards the bands
		mfcc_coeffs = mfcc(frameSpectrum)[1]

		# Adds the values to the pool
		pool.add('ZCR', frameZCR)
		pool.add('SC', frameSC)
		pool.add('Flat', frameEFlatness)
		pool.add('LAtt', frameLogAtt)
		pool.add('SDec', frameStrDec)
		for index, coef in enumerate(frameLPC[1:frameLPC.size]):
			pool.add('LPC' + str(index + 1), coef)
		for index, coef in enumerate(frameReflection):
			pool.add('REFL' + str(index), coef)

		pool.add('SR', frameSR)
		pool.add('SF', frameSF)
		pool.add('SEFlat', frameSEFlatness)
		pool.add('SFlat', frameSFlat)
		pool.add('SLAtt', frameSLogAtt)
		pool.add('SSDec', frameSStrDec)
		pool.add('HFC', frameHFC)
		pool.add('SComp', frameSComp)

		for index, coef in enumerate(mfcc_coeffs):
			pool.add('mfcc' + str(index), coef)

	YamlOutput(filename = outputPath, format = 'json', writeVersion = False)(pool)

# Prints a nice message to let the user know the module was imported
print('feature_extractor loaded')

# Enables executing the module as a standalone script
if __name__ == "__main__":
	import sys
	extractFeatures(sys.argv[1], sys.argv[2], int(sys.argv[3]))