def extractTemporalFeature(wavedata):
"""time domain features"""
zcrs = []
rmss = []
for frame in sliding_window(wavedata, 1024, 512):
zcrs.append(zcr(frame, np.max(np.abs(wavedata))))
rmss.append(rms(frame))
return [zcrs, rmss]
def extractTemporalFeature(wavedata):
"""time domain features"""
zcrs = []
rmss = []
for frame in sliding_window(wavedata, 1024, 512):
zcrs.append(zcr(frame, np.max(np.abs(wavedata))))
rmss.append(rms(frame))
return [zcrs, rmss]
def temporalCentroid(wavedata):
"""
calculate the center of mass in the temporal energy envelop and return the ratio to the total length of signal
the hop size is 1024
"""
rmss = []
timp = len(wavedata) / 44100.0
for frame in sliding_window(wavedata, 1024, 1024):
rmss.append(energy(frame))
t = np.linspace(0,timp, len(rmss))
rmss = np.array(rmss)
return np.sum((t * rmss))/np.sum(rmss) / timp
def temporalCentroid(wavedata):
"""
calculate the center of mass in the temporal energy envelop and return the ratio to the total length of signal
the hop size is 1024
"""
rmss = []
timp = len(wavedata) / 44100.0
for frame in sliding_window(wavedata, 1024, 1024):
rmss.append(energy(frame))
t = np.linspace(0, timp, len(rmss))
rmss = np.array(rmss)
return np.sum((t * rmss)) / np.sum(rmss) / timp
