def separate_effective(self,
wave: Wave,
feature: AcousticFeature,
threshold=None):
"""
:return: (effective feature, effective flags)
"""
hop, length = wave.get_hop_and_length(
frame_period=self._param.frame_period)
if threshold is None:
if self._param.threshold_db is not None:
effective = wave.get_effective_frame(
threshold_db=self._param.threshold_db,
fft_length=self._param.fft_length,
frame_period=self._param.frame_period,
)
feature = feature.indexing(effective)
else:
effective = numpy.ones(length, dtype=bool)
else:
mse = librosa.feature.rmse(y=wave.wave,
frame_length=self._param.fft_length,
hop_length=hop)**2
effective = (librosa.core.power_to_db(mse.squeeze()) > -threshold)
if len(effective) < len(feature.f0): # the divide move
effective = numpy.r_[effective, False]
if len(effective) > len(feature.f0): # the divide move
effective = effective
if len(effective) < len(feature.f0): # the divide move
effective = numpy.r_[effective, False]
if len(effective) > len(feature.f0): # the divide move
effective = effective
feature = feature.indexing(effective)
return feature, effective
def convert_loop(self, in_feature: AcousticFeature, n_len: int = 512, n_wrap: int = 128):
out_feature_list: List[AcousticFeature] = []
N = len(in_feature.f0)
for i in numpy.arange(0, int(numpy.ceil(N / n_len))):
# convert with overwrapped
start = i * n_len
mi = max(start - n_wrap, 0)
ma = min(start + n_len + n_wrap, N)
f = in_feature.indexing(numpy.arange(mi, ma))
o_warp = self.convert(f)
# eliminate overwrap
ex_mi = start - mi
ex_len = min(ma - start, n_len)
o = o_warp.indexing(numpy.arange(ex_mi, ex_mi + ex_len))
out_feature_list.append(o)
return AcousticFeature.concatenate(out_feature_list)