def edit_with_default_desc(self, unmixed_track):
selected_tracks = [unmixed_track[i] for i in self.targets_index]
linear_sum = np.sum(unmixed_track, axis=0)
manipulated_linear_sum = np.sum(selected_tracks, axis=0)
linear_sum, manipulated_linear_sum = normalize(linear_sum,
manipulated_linear_sum)
return self.gen_desc_default(), linear_sum, manipulated_linear_sum
def __edit__(self, unmixed_track: np.ndarray):
manipulated_track = np.copy(unmixed_track)
for idx in self.targets_index:
manipulated_track[idx] = self.snd_fx(manipulated_track[idx])
linear_sum = np.sum(unmixed_track, axis=0)
manipulated_linear_sum = np.sum(manipulated_track, axis=0)
linear_sum, manipulated_linear_sum = normalize(linear_sum,
manipulated_linear_sum)
return linear_sum, manipulated_linear_sum
def edit_with_default_desc(self, unmixed_track):
manipulated_track = np.copy(unmixed_track)
for idx in self.targets_index:
manipulated_track[idx] *= .5
linear_sum = np.sum(unmixed_track, axis=0)
manipulated_linear_sum = np.sum(manipulated_track, axis=0)
linear_sum, manipulated_linear_sum = normalize(linear_sum, manipulated_linear_sum)
return self.gen_desc_default(), linear_sum, manipulated_linear_sum
def edit_with_default_desc(self, unmixed_track):
manipulated_track = np.copy(unmixed_track)
for idx in self.targets_index:
mean = manipulated_track[idx].mean(axis=-1)
manipulated_track[idx, :, 0] = mean * 1.0
manipulated_track[idx, :, 1] = mean * 0.0
linear_sum = np.sum(unmixed_track, axis=0)
manipulated_linear_sum = np.sum(manipulated_track, axis=0)
linear_sum, manipulated_linear_sum = normalize(linear_sum,
manipulated_linear_sum)
return self.gen_desc_default(), linear_sum, manipulated_linear_sum
def edit_with_default_desc(self, unmixed_track):
manipulated_track = np.copy(unmixed_track)
for idx in range(3):
if idx in self.targets_index:
# unmixed_track[idx] = self.snd_fx(unmixed_track[idx])
# else:
manipulated_track[idx] = self.snd_fx(manipulated_track[idx])
# unmixed_track[idx] = self.snd_fx(unmixed_track[idx])
linear_sum = np.sum(unmixed_track, axis=0)
manipulated_linear_sum = np.sum(manipulated_track, axis=0)
linear_sum, manipulated_linear_sum = normalize(linear_sum,
manipulated_linear_sum)
return self.gen_desc_default(), manipulated_linear_sum, linear_sum
