asteroid.losses.multi_scale_spectral module¶
-
class
asteroid.losses.multi_scale_spectral.
SingleSrcMultiScaleSpectral
(n_filters=None, windows_size=None, hops_size=None, alpha=1.0)[source]¶ Bases:
sphinx.ext.autodoc.importer._MockObject
Measure multi-scale spectral loss as described in [1]
Parameters: - Shape:
- est_targets : \((batch, time)\).
- targets: \((batch, time)\).
Returns: torch.Tensor
– with shape [batch]- Examples
>>> import torch >>> targets = torch.randn(10, 32000) >>> est_targets = torch.randn(10, 32000) >>> # Using it by itself on a pair of source/estimate >>> loss_func = SingleSrcMultiScaleSpectral() >>> loss = loss_func(est_targets, targets)
>>> import torch >>> from asteroid.losses import PITLossWrapper >>> targets = torch.randn(10, 2, 32000) >>> est_targets = torch.randn(10, 2, 32000) >>> # Using it with PITLossWrapper with sets of source/estimates >>> loss_func = PITLossWrapper(SingleSrcMultiScaleSpectral(), >>> pit_from='pw_pt') >>> loss = loss_func(est_targets, targets)
- References
- [1] Jesse Engel and Lamtharn (Hanoi) Hantrakul and Chenjie Gu and Adam Roberts “DDSP: Differentiable Digital Signal Processing” ICLR 2020.