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asteroid.dsp.consistency module

asteroid.dsp.consistency.mixture_consistency(mixture, est_sources, src_weights=None, dim=1)[source]

Applies mixture consistency to a tensor of estimated sources.

Args

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.
Notes
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.
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)
References
Scott Wisdom, John R Hershey, Kevin Wilson, Jeremy Thorpe, Michael Chinen, Brian Patton, and Rif A Saurous. “Differentiable consistency constraints for improved deep speech enhancement”, ICASSP 2019.
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