def __call__(self, mspec, lseg, difflen = 0):
"""
*** input
* mspec: mel spectrogram
* lseg: list of tuples (label, start, stop) corresponding to previous segmentations
* difflen: 0 if the original length of the mel spectrogram is >= 68
otherwise it is set to 68 - length(mspec)
*** output
a list of adjacent tuples (label, start, stop)
"""
if self.nmel < 24:
mspec = mspec[:, :self.nmel].copy()
patches, finite = _get_patches(mspec, 68, 2)
if difflen > 0:
patches = patches[:-int(difflen / 2), :, :]
finite = finite[:-int(difflen / 2)]
assert len(finite) == len(patches), (len(patches), len(finite))
ret = []
for lab, start, stop in lseg:
if lab != self.inlabel:
ret.append((lab, start, stop))
continue
rawpred = self.nn.predict(patches[start:stop, :])
rawpred[finite[start:stop] == False, :] = 0.5
pred = viterbi_decoding(np.log(rawpred), diag_trans_exp(self.viterbi_arg, len(self.outlabels)))
for lab2, start2, stop2 in _binidx2seglist(pred):
ret.append((self.outlabels[int(lab2)], start2+start, stop2+start))
return ret
def _gender(nn, patches, finite_patches, speechzicseg):
ret = []
for lab, start, stop in speechzicseg:
if lab in ['Music', 'NOACTIVITY']:
# no energy
ret.append((lab, start, stop))
continue
rawpred = nn.predict(patches[start:stop, :])
rawpred[finite_patches[start:stop] == False, :] = 0.5
pred = viterbi_decoding(np.log(rawpred), log_trans_exp(80))
for lab2, start2, stop2 in _binidx2seglist(pred):
ret.append((['Female',
'Male'][int(lab2)], start2 + start, stop2 + start))
return ret
def __call__(self, mspec, lseg, difflen = 0):
"""
*** input
* mspec: mel spectrogram
* lseg: list of tuples (label, start, stop) corresponding to previous segmentations
* difflen: 0 if the original length of the mel spectrogram is >= 68
otherwise it is set to 68 - length(mspec)
*** output
a list of adjacent tuples (label, start, stop)
"""
if self.nmel < 24:
mspec = mspec[:, :self.nmel].copy()
patches, finite = _get_patches(mspec, 68, 2)
if difflen > 0:
patches = patches[:-int(difflen / 2), :, :]
finite = finite[:-int(difflen / 2)]
assert len(finite) == len(patches), (len(patches), len(finite))
batch = []
for lab, start, stop in lseg:
if lab == self.inlabel:
batch.append(patches[start:stop, :])
if len(batch) > 0:
batch = np.concatenate(batch)
with graph.as_default():
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
rawpred = self.nn.predict(batch, batch_size=self.batch_size)
ret = []
for lab, start, stop in lseg:
if lab != self.inlabel:
ret.append((lab, start, stop))
continue
l = stop - start
r = rawpred[:l]
rawpred = rawpred[l:]
r[finite[start:stop] == False, :] = 0.5
pred = viterbi_decoding(np.log(r), diag_trans_exp(self.viterbi_arg, len(self.outlabels)))
for lab2, start2, stop2 in _binidx2seglist(pred):
ret.append((self.outlabels[int(lab2)], start2+start, stop2+start))
return ret
def _speechzic(nn, patches, finite_patches, vad):
ret = []
for lab, start, stop in _binidx2seglist(vad):
if lab == 0:
# no energy
ret.append(('NOACTIVITY', start, stop))
continue
#print(start, stop)
rawpred = nn.predict(patches[start:stop, :])
rawpred[finite_patches[start:stop] == False, :] = 0.5
pred = viterbi_decoding(np.log(rawpred), log_trans_exp(150))
for lab2, start2, stop2 in _binidx2seglist(pred):
ret.append((['Speech',
'Music'][int(lab2)], start2 + start, stop2 + start))
return ret
def _energy_activity(loge, ratio=0.03):
threshold = np.mean(loge[np.isfinite(loge)]) + np.log(ratio)
raw_activity = (loge > threshold)
return viterbi_decoding(pred2logemission(raw_activity),
log_trans_exp(150, cost0=-5))
