asteroid.dsp.consistency module

asteroid.dsp.consistency.mixture_consistency(mixture: <sphinx.ext.autodoc.importer._MockObject object at 0x7f85d61896d0>, est_sources: <sphinx.ext.autodoc.importer._MockObject object at 0x7f85d6189510>, src_weights: Optional[<sphinx.ext.autodoc.importer._MockObject object at 0x7f85d6189810>] = None, dim: int = 1) → <sphinx.ext.autodoc.importer._MockObject object at 0x7f85d6189b10>

Applies mixture consistency to a tensor of estimated sources.

Parameters:

• mixture (torch.Tensor) – Mixture waveform or TF representation.
• est_sources (torch.Tensor) – Estimated sources waveforms or TF representations.
• src_weights (torch.Tensor) – Consistency weight for each source. Shape needs to be broadcastable to est_source. We make sure that the weights sum up to 1 along dim dim. If src_weights is None, compute them based on relative power.
• dim (int) – Axis which contains the sources in est_sources.

Returns

torch.Tensor with same shape as est_sources, after applying mixture consistency.

Examples

>>> # Works on waveforms
>>> mix = torch.randn(10, 16000)
>>> est_sources = torch.randn(10, 2, 16000)
>>> new_est_sources = mixture_consistency(mix, est_sources, dim=1)
>>> # Also works on spectrograms
>>> mix = torch.randn(10, 514, 400)
>>> est_sources = torch.randn(10, 2, 514, 400)
>>> new_est_sources = mixture_consistency(mix, est_sources, dim=1)

Note

This method can be used only in ‘complete’ separation tasks, otherwise the residual error will contain unwanted sources. For example, this won’t work with the task “sep_noisy” from WHAM.

References

Scott Wisdom et al. “Differentiable consistency constraints for improved deep speech enhancement”, ICASSP 2019.