ssspy.bss.admmbss#

Algorithms#

class ssspy.bss.admmbss.ADMMBSSBase(penalty_fn=None, prox_penalty=None, callbacks=None, scale_restoration=True, record_loss=True, reference_id=0)#

Base class of blind source separation via alternative direction method of multiplier.

Parameters:

  • penalty_fn (callable) – Penalty function that determines source model.

  • prox_penalty (callable) – Proximal operator of penalty function. Default: None.

  • callbacks (callable or list[callable], optional) – Callback functions. Each function is called before separation and at each iteration. Default: None.

  • scale_restoration (bool or str) – Technique to restore scale ambiguity. If scale_restoration=True, the projection back technique is applied to estimated spectrograms. You can also specify projection_back explicitly. Default: True.

  • record_loss (bool) – Record the loss at each iteration of the update algorithm if record_loss=True. Default: True.

  • reference_id (int) – Reference channel for projection back. Default: 0.

class ssspy.bss.admmbss.ADMMBSS(rho=1, alpha=None, relaxation=1, penalty_fn=None, prox_penalty=None, callbacks=None, scale_restoration=True, record_loss=True, reference_id=0)#

Base class of blind source separation via alternative direction method of multiplier.

Parameters:

  • rho (float) – Penalty parameter. Default: 1.

  • alpha (float) – Relaxation parameter (deprecated). Set relaxation instead.

  • relaxation (float) – Relaxation parameter. Default: 1.

  • penalty_fn (callable) – Penalty function that determines source model.

  • prox_penalty (callable) – Proximal operator of penalty function. Default: None.

  • callbacks (callable or list[callable], optional) – Callback functions. Each function is called before separation and at each iteration. Default: None.

  • scale_restoration (bool or str) – Technique to restore scale ambiguity. If scale_restoration=True, the projection back technique is applied to estimated spectrograms. You can also specify projection_back explicitly. Default: True.

  • record_loss (bool) – Record the loss at each iteration of the update algorithm if record_loss=True. Default: True.

  • reference_id (int) – Reference channel for projection back. Default: 0.

__call__(input, n_iter=100, initial_call=True, **kwargs)#

Separate a frequency-domain multichannel signal.

Parameters:

  • input (numpy.ndarray) – Mixture signal in frequency-domain. The shape is (n_channels, n_bins, n_frames).

  • n_iter (int) – Number of iterations of demixing filter updates. Default: 100.

  • initial_call (bool) – If True, perform callbacks (and computation of loss if necessary) before iterations.

Return type:

ndarray

Returns:

numpy.ndarray of the separated signal in frequency-domain. The shape is (n_channels, n_bins, n_frames).

update_once()#

Update demixing filters, auxiliary parameters, and dual parameters once.

Return type:

None