1.a. 特征提取(数据预处理)
在这里,我们展示了语音识别和图像识别中使用的共同特征。 音频功能:时间特征,光谱特征。 图像特征:边缘,角落/兴趣点,斑点/感兴趣区域,脊。
时间特征
class TF(object):
def __init__(self, nfilt=40, ncep=13,
lowerf=133.3333, upperf=6855.4976, alpha=0.97,
samprate=16000, frate=100, wlen=0.0256,
nfft=512):
# Store parameters
self.lowerf = lowerf
self.upperf = upperf
self.nfft = nfft
self.ncep = ncep
self.nfilt = nfilt
self.frate = frate
self.fshift = float(samprate) / frate
# Build Hamming window
self.wlen = int(wlen * samprate)
self.win = numpy.hamming(self.wlen)
# Prior sample for pre-emphasis
self.prior = 0
self.alpha = alpha
# Build mel filter matrix
self.filters = numpy.zeros((nfft/2+1,nfilt), 'd')
dfreq = float(samprate) / nfft
if upperf > samprate/2:
raise(Exception,
"Upper frequency %f exceeds Nyquist %f" % (upperf, samprate/2))
melmax = mel(upperf)
melmin = mel(lowerf)
dmelbw = (melmax - melmin) / (nfilt + 1)
# Filter edges, in Hz
filt_edge = melinv(melmin + dmelbw * numpy.arange(nfilt + 2, dtype='d'))
for whichfilt in range(0, nfilt):
# Filter triangles, in DFT points
leftfr = round(filt_edge[whichfilt] / dfreq)
centerfr = round(filt_edge[whichfilt + 1] / dfreq)
rightfr = round(filt_edge[whichfilt + 2] / dfreq)
# For some reason this is calculated in Hz, though I think
# it doesn't really matter
fwidth = (rightfr - leftfr) * dfreq
height = 2. / fwidth
if centerfr != leftfr:
leftslope = height / (centerfr - leftfr)
else:
leftslope = 0
freq = leftfr + 1
while freq < centerfr:
self.filters[freq,whichfilt] = (freq - leftfr) * leftslope
freq = freq + 1
if freq == centerfr: # This is always true
self.filters[freq,whichfilt] = height
freq = freq + 1
if centerfr != rightfr:
rightslope = height / (centerfr - rightfr)
while freq < rightfr:
self.filters[freq,whichfilt] = (freq - rightfr) * rightslope
freq = freq + 1
# Build DCT matrix
self.s2dct = s2dctmat(nfilt, ncep, 1./nfilt)
def sig2s2mfc(self, sig):
nfr = int(len(sig) / self.fshift + 1)
mfcc = numpy.zeros((nfr, self.ncep), 'd')
fr = 0
while fr < nfr:
start = round(fr * self.fshift)
end = min(len(sig), start + self.wlen)
frame = sig[start:end]
if len(frame) < self.wlen:
frame = numpy.resize(frame,self.wlen)
frame[self.wlen:] = 0
mfcc[fr] = self.frame2s2mfc(frame)
fr = fr + 1
return mfcc
def sig2logspec(self, sig):
nfr = int(len(sig) / self.fshift + 1)
mfcc = numpy.zeros((nfr, self.nfilt), 'd')
fr = 0
while fr < nfr:
start = round(fr * self.fshift)
end = min(len(sig), start + self.wlen)
frame = sig[start:end]
if len(frame) < self.wlen:
frame = numpy.resize(frame,self.wlen)
frame[self.wlen:] = 0
mfcc[fr] = self.frame2logspec(frame)
fr = fr + 1
return mfcc
def pre_emphasis(self, frame):
# FIXME: Do this with matrix multiplication
outfr = numpy.empty(len(frame), 'd')
outfr[0] = frame[0] - self.alpha * self.prior
for i in range(1,len(frame)):
outfr[i] = frame[i] - self.alpha * frame[i-1]
self.prior = frame[-1]
return outfr
def frame2logspec(self, frame):
frame = self.pre_emphasis(frame) * self.win
fft = numpy.fft.rfft(frame, self.nfft)
# Square of absolute value
power = fft.real * fft.real + fft.imag * fft.imag
return numpy.log(numpy.dot(power, self.filters).clip(1e-5,numpy.inf))
def frame2s2mfc(self, frame):
logspec = self.frame2logspec(frame)
return numpy.dot(logspec, self.s2dct.T) / self.nfilt
def s2dctmat(nfilt,ncep,freqstep):
"""Return the 'legacy' not-quite-DCT matrix used by Sphinx"""
melcos = numpy.empty((ncep, nfilt), 'double')
for i in range(0,ncep):
freq = numpy.pi * float(i) / nfilt
melcos[i] = numpy.cos(freq * numpy.arange(0.5, float(nfilt)+0.5, 1.0, 'double'))
melcos[:,0] = melcos[:,0] * 0.5
return melcos