def compute(self, waveforms, window_length, increment, samplerate=16000, min_freq=0, max_freq=None, nstd=6, offset=50):
signal, stimulus = waveforms
self.time, self.frequencies, spec = gaussian_stft(signal,
samplerate,
window_length,
increment,
min_freq=min_freq,
max_freq=max_freq,
nstd=nstd)[:3]
stim_spec = gaussian_stft(stimulus,
samplerate,
window_length,
increment,
min_freq=min_freq,
max_freq=max_freq,
nstd=nstd)[2]
ratio_mask = np.abs(spec) ** self.alpha / np.abs(stim_spec) ** self.alpha
ratio_mask = np.maximum(0, np.minimum(1, ratio_mask)).T
stim_spec = log_spectrogram(np.abs(stim_spec), offset=offset).T
self.mean = np.mean(stim_spec, axis=0)
stim_spec -= self.mean
return stim_spec, ratio_mask
def compute(self, waveform, window_length, increment, samplerate=16000,
min_freq=0, max_freq=None, nstd=6, offset=50):
self.time, self.frequencies, spec = gaussian_stft(waveform,
samplerate,
window_length,
increment,
min_freq=min_freq,
max_freq=max_freq,
nstd=nstd)[:3]
return log_spectrogram(np.abs(spec), offset=offset).T
