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167 lines
9.1 KiB
167 lines
9.1 KiB
# calculate filterbank features. Provides e.g. fbank and mfcc features for use in ASR applications
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# Author: James Lyons 2012
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from __future__ import division
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import numpy
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from python_speech_features import sigproc
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from scipy.fftpack import dct
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def mfcc(signal,samplerate=16000,winlen=0.025,winstep=0.01,numcep=13,
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nfilt=23,nfft=512,lowfreq=20,highfreq=None,dither=1.0,remove_dc_offset=True,preemph=0.97,
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ceplifter=22,useEnergy=True,wintype='povey'):
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"""Compute MFCC features from an audio signal.
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:param signal: the audio signal from which to compute features. Should be an N*1 array
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:param samplerate: the samplerate of the signal we are working with.
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:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
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:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
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:param numcep: the number of cepstrum to return, default 13
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:param nfilt: the number of filters in the filterbank, default 26.
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:param nfft: the FFT size. Default is 512.
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:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
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:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
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:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
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:param ceplifter: apply a lifter to final cepstral coefficients. 0 is no lifter. Default is 22.
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:param appendEnergy: if this is true, the zeroth cepstral coefficient is replaced with the log of the total frame energy.
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:param winfunc: the analysis window to apply to each frame. By default no window is applied. You can use numpy window functions here e.g. winfunc=numpy.hamming
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:returns: A numpy array of size (NUMFRAMES by numcep) containing features. Each row holds 1 feature vector.
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"""
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feat,energy = fbank(signal,samplerate,winlen,winstep,nfilt,nfft,lowfreq,highfreq,dither,remove_dc_offset,preemph,wintype)
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feat = numpy.log(feat)
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feat = dct(feat, type=2, axis=1, norm='ortho')[:,:numcep]
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feat = lifter(feat,ceplifter)
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if useEnergy: feat[:,0] = numpy.log(energy) # replace first cepstral coefficient with log of frame energy
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return feat
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def fbank(signal,samplerate=16000,winlen=0.025,winstep=0.01,
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nfilt=40,nfft=512,lowfreq=0,highfreq=None,dither=1.0,remove_dc_offset=True, preemph=0.97,
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wintype='hamming'):
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"""Compute Mel-filterbank energy features from an audio signal.
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:param signal: the audio signal from which to compute features. Should be an N*1 array
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:param samplerate: the samplerate of the signal we are working with.
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:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
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:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
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:param nfilt: the number of filters in the filterbank, default 26.
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:param nfft: the FFT size. Default is 512.
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:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
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:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
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:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
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:param winfunc: the analysis window to apply to each frame. By default no window is applied. You can use numpy window functions here e.g. winfunc=numpy.hamming
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winfunc=lambda x:numpy.ones((x,))
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:returns: 2 values. The first is a numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector. The
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second return value is the energy in each frame (total energy, unwindowed)
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"""
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highfreq= highfreq or samplerate/2
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frames,raw_frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate, dither, preemph, remove_dc_offset, wintype)
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pspec = sigproc.powspec(frames,nfft) # nearly the same until this part
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energy = numpy.sum(raw_frames**2,1) # this stores the raw energy in each frame
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energy = numpy.where(energy == 0,numpy.finfo(float).eps,energy) # if energy is zero, we get problems with log
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fb = get_filterbanks(nfilt,nfft,samplerate,lowfreq,highfreq)
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feat = numpy.dot(pspec,fb.T) # compute the filterbank energies
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feat = numpy.where(feat == 0,numpy.finfo(float).eps,feat) # if feat is zero, we get problems with log
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return feat,energy
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def logfbank(signal,samplerate=16000,winlen=0.025,winstep=0.01,
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nfilt=40,nfft=512,lowfreq=64,highfreq=None,dither=1.0,remove_dc_offset=True,preemph=0.97,wintype='hamming'):
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"""Compute log Mel-filterbank energy features from an audio signal.
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:param signal: the audio signal from which to compute features. Should be an N*1 array
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:param samplerate: the samplerate of the signal we are working with.
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:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
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:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
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:param nfilt: the number of filters in the filterbank, default 26.
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:param nfft: the FFT size. Default is 512.
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:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
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:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
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:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
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:returns: A numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector.
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"""
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feat,energy = fbank(signal,samplerate,winlen,winstep,nfilt,nfft,lowfreq,highfreq,dither, remove_dc_offset,preemph,wintype)
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return numpy.log(feat)
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def hz2mel(hz):
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"""Convert a value in Hertz to Mels
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:param hz: a value in Hz. This can also be a numpy array, conversion proceeds element-wise.
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:returns: a value in Mels. If an array was passed in, an identical sized array is returned.
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"""
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return 1127 * numpy.log(1+hz/700.0)
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def mel2hz(mel):
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"""Convert a value in Mels to Hertz
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:param mel: a value in Mels. This can also be a numpy array, conversion proceeds element-wise.
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:returns: a value in Hertz. If an array was passed in, an identical sized array is returned.
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"""
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return 700 * (numpy.exp(mel/1127.0)-1)
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def get_filterbanks(nfilt=26,nfft=512,samplerate=16000,lowfreq=0,highfreq=None):
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"""Compute a Mel-filterbank. The filters are stored in the rows, the columns correspond
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to fft bins. The filters are returned as an array of size nfilt * (nfft/2 + 1)
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:param nfilt: the number of filters in the filterbank, default 20.
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:param nfft: the FFT size. Default is 512.
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:param samplerate: the samplerate of the signal we are working with. Affects mel spacing.
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:param lowfreq: lowest band edge of mel filters, default 0 Hz
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:param highfreq: highest band edge of mel filters, default samplerate/2
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:returns: A numpy array of size nfilt * (nfft/2 + 1) containing filterbank. Each row holds 1 filter.
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"""
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highfreq= highfreq or samplerate/2
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assert highfreq <= samplerate/2, "highfreq is greater than samplerate/2"
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# compute points evenly spaced in mels
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lowmel = hz2mel(lowfreq)
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highmel = hz2mel(highfreq)
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# check kaldi/src/feat/Mel-computations.h
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fbank = numpy.zeros([nfilt,nfft//2+1])
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mel_freq_delta = (highmel-lowmel)/(nfilt+1)
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for j in range(0,nfilt):
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leftmel = lowmel+j*mel_freq_delta
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centermel = lowmel+(j+1)*mel_freq_delta
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rightmel = lowmel+(j+2)*mel_freq_delta
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for i in range(0,nfft//2):
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mel=hz2mel(i*samplerate/nfft)
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if mel>leftmel and mel<rightmel:
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if mel<centermel:
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fbank[j,i]=(mel-leftmel)/(centermel-leftmel)
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else:
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fbank[j,i]=(rightmel-mel)/(rightmel-centermel)
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return fbank
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def lifter(cepstra, L=22):
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"""Apply a cepstral lifter the the matrix of cepstra. This has the effect of increasing the
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magnitude of the high frequency DCT coeffs.
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:param cepstra: the matrix of mel-cepstra, will be numframes * numcep in size.
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:param L: the liftering coefficient to use. Default is 22. L <= 0 disables lifter.
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"""
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if L > 0:
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nframes,ncoeff = numpy.shape(cepstra)
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n = numpy.arange(ncoeff)
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lift = 1 + (L/2.)*numpy.sin(numpy.pi*n/L)
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return lift*cepstra
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else:
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# values of L <= 0, do nothing
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return cepstra
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def delta(feat, N):
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"""Compute delta features from a feature vector sequence.
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:param feat: A numpy array of size (NUMFRAMES by number of features) containing features. Each row holds 1 feature vector.
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:param N: For each frame, calculate delta features based on preceding and following N frames
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:returns: A numpy array of size (NUMFRAMES by number of features) containing delta features. Each row holds 1 delta feature vector.
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"""
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if N < 1:
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raise ValueError('N must be an integer >= 1')
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NUMFRAMES = len(feat)
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denominator = 2 * sum([i**2 for i in range(1, N+1)])
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delta_feat = numpy.empty_like(feat)
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padded = numpy.pad(feat, ((N, N), (0, 0)), mode='edge') # padded version of feat
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for t in range(NUMFRAMES):
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delta_feat[t] = numpy.dot(numpy.arange(-N, N+1), padded[t : t+2*N+1]) / denominator # [t : t+2*N+1] == [(N+t)-N : (N+t)+N+1]
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return delta_feat
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