def auto_correlogram(x, filter_bank, correlation_window=Milliseconds(30)):
n_filters = filter_bank.shape[0]
filter_size = filter_bank.shape[1]
corr_win_samples = int(correlation_window / x.samplerate.frequency)
windowed = sliding_window(x, filter_size, 1, flatten=False)
print(windowed.shape)
filtered = np.dot(windowed, filter_bank.T)
print(filtered.shape)
corr = sliding_window(filtered,
ws=(corr_win_samples, n_filters),
ss=(1, n_filters),
flatten=False)
print(corr.shape)
padded_shape = list(corr.shape)
padded_shape[2] = corr_win_samples * 2
padded = np.zeros(padded_shape, dtype=np.float32)
padded[:, :, :corr_win_samples, :] = corr
print(padded.shape)
coeffs = np.fft.fft(padded, axis=2, norm='ortho')
correlated = np.fft.ifft(np.abs(coeffs)**2, axis=2, norm='ortho')
return np.concatenate([
correlated[:, :, corr_win_samples:, :],
correlated[:, :, :corr_win_samples, :],
],
axis=2)
return correlated
def pitch_shift(x, semitones, frame_sample_rate=None):
original_shape = x.shape[1] if x.ndim == 2 else x.shape[0]
# first, perform a time stretch so that the audio will have the desired
# pitch
factor = 2.0**(-float(semitones) / 12.0)
stretched = time_stretch(x, factor, frame_sample_rate=frame_sample_rate)
# hang on to original dimensions
dimensions = stretched.dimensions
# window the audio using a power-of-2 frame size for more efficient FFT
# computations
batch_size = stretched.shape[0]
window_size = 1024
step = (1, window_size)
new_window_shape = int(window_size * factor)
padding = window_size - int(stretched.shape[-1] % window_size)
stretched = np.pad(stretched, ((0, 0), (0, padding)), mode='constant')
windowed = sliding_window(stretched, step, step, flatten=False).squeeze()
# resample the audio so that it has the correct duration
rs = resample(windowed, new_window_shape, axis=-1)
# flatten out the windowed, resampled audio
rs = rs.reshape(batch_size, -1)
# slice the audio to remove residual zeros resulting from our power-of-2
# zero padding above
rs = rs[:, :original_shape]
return ArrayWithUnits(rs, dimensions)
def sliding_window(self, windowsize, stepsize=None):
ws = tuple(self._compute_span(windowsize))
ss = tuple(self._compute_span(stepsize)) if stepsize else ws
result = sliding_window(self, ws, ss)
try:
new_dims = tuple(self._compute_new_dims(result, ws, ss))
except ValueError:
new_dims = [IdentityDimension()] * result.ndim
return ArrayWithUnits(result, new_dims)
def sliding_window(self, windowsize, stepsize=None):
ws = tuple(self._compute_span(windowsize))
ss = tuple(self._compute_span(stepsize)) if stepsize else ws
result = sliding_window(self, ws, ss)
try:
new_dims = tuple(self._compute_new_dims(result, ws, ss))
except ValueError:
new_dims = [IdentityDimension()] * result.ndim
return ArrayWithUnits(result, new_dims)