def mix_feature(tup):
mfcc = MFCC.extract(tup)
lpc = LPC.extract(tup)
if len(mfcc) == 0:
print >> sys.stderr, "ERROR.. failed to extract mfcc feature:", len(
tup[1])
return np.concatenate((mfcc, lpc), axis=1)
def mix_feature(self):
mfcc = MFCC.extract(self.FS, self.signal)
lpc = LPC.extract(self.FS, self.signal)
#if len(mfcc) == 0:
# print >> sys.stderr, "ERROR.. failed to extract mfcc feature:", len(tup[1])
#print "mfcc ",mfcc
#print "lpc ",lpc
return np.concatenate((mfcc, lpc), axis=1)
def mix_feature(tup):
mfcc = MFCC.extract(tup)
lpc = LPC.extract(tup)
mfcc_1dif_coef = differentiate(mfcc)
mfcc_2dif_coef = differentiate(mfcc_1dif_coef)
if len(mfcc) == 0:
print >> sys.stderr, "ERROR.. failed to extract mfcc feature:", len(tup[1])
#pdb.set_trace()
#return np.concatenate((mfcc, lpc), axis=1) # 28 dimension
# 39 dimension: mfcc 0-12 coefficient, and 13 first-order differential coefficient
# 13 second-order differential coefficient
return np.concatenate((mfcc[:,0:13], mfcc_1dif_coef[:,0:13], mfcc_2dif_coef), axis=1)
def getFeaturesFromWave(self, fname): fs, signal = scipy.io.wavfile.read(fname) window_len = self.frame_size*fs # Number of samples in frame_size sample_shift = self.frame_shift*fs # Number of samples shifted try: if signal.shape[1]: signal = numpy.mean(signal, axis=1) except: print "single column" segmentLimits = rs.silenceRemoval(signal, fs, self.frame_size, self.frame_shift) segmentLimits = numpy.asarray(segmentLimits) data = rs.nonsilentRegions(segmentLimits, fs, signal) stfeatures = featureExtraction.stFeatureExtraction(data, fs, window_len, sample_shift ) lpc = LPC.extract((fs, data)) featuresT = stfeatures.transpose() featuresT = numpy.concatenate((featuresT, lpc), axis = 1) return featuresT
def getFeaturesFromWave(self, fname):
fs, signal = scipy.io.wavfile.read(fname)
window_len = self.frame_size * fs # Number of samples in frame_size
sample_shift = self.frame_shift * fs # Number of samples shifted
try:
if signal.shape[1]:
signal = numpy.mean(signal, axis=1)
except:
print "single column"
segmentLimits = rs.silenceRemoval(signal, fs, self.frame_size,
self.frame_shift)
segmentLimits = numpy.asarray(segmentLimits)
data = rs.nonsilentRegions(segmentLimits, fs, signal)
stfeatures = featureExtraction.stFeatureExtraction(
data, fs, window_len, sample_shift)
lpc = LPC.extract((fs, data))
featuresT = stfeatures.transpose()
featuresT = numpy.concatenate((featuresT, lpc), axis=1)
return featuresT
def mix_feature(tup):
mfcc = MFCC.extract(tup)
lpc = LPC.extract(tup)
if len(mfcc) == 0:
print >> sys.stderr, "ERROR.. failed to extract mfcc feature:", len(tup[1])
return np.concatenate((mfcc, lpc), axis=1)
def mix_feature(tup):
bob = BOB.extract(tup)
lpc = LPC.extract(tup)
if len(bob) == 0:
print len(tup[1])
return np.concatenate((bob, lpc), axis=1)
def mix_feature(tup):
bob = BOB.extract(tup)
lpc = LPC.extract(tup)
if len(bob) == 0:
print len(tup[1])
return np.concatenate((bob, lpc), axis=1)
