asteroid.losses.stoi module¶

class asteroid.losses.stoi.NegSTOILoss(*args, **kwargs)[source]¶

Bases: sphinx.ext.autodoc.importer._MockObject

Negated Short Term Objective Intelligibility (STOI) metric, to be used: as a loss function. Inspired from [1, 2, 3] but not exactly the same : cannot be used as the STOI metric directly (use pystoi instead). See Notes.

Parameters:	sample_rate (int) – sample rate of the audio files use_vad (bool) – Whether to use simple VAD (see Notes) extended (bool) – Whether to compute extended version [3].

Shapes:: (time,) –> (1, ) (batch, time) –> (batch, ) (batch, n_src, time) –> (batch, n_src)

Returns:	torch.Tensor of shape (batch, *, ), only the time dimension has been reduced.

Note

In the NumPy version, some kind of simple VAD was used to remove the silent frames before chunking the signal into short-term envelope vectors. We don’t do the same here because removing frames in a batch is cumbersome and inefficient. If use_vad is set to True, instead we detect the silent frames and keep a mask tensor. At the end, the normalized correlation of short-term envelope vectors is masked using this mask (unfolded) and the mean is computed taking the mask values into account.

Examples

>>> import torch
>>> from asteroid.losses import PITLossWrapper
>>> targets = torch.randn(10, 2, 32000)
>>> est_targets = torch.randn(10, 2, 32000)
>>> loss_func = PITLossWrapper(NegSTOILoss(sample_rate=8000), pit_from='pw_pt')
>>> loss = loss_func(est_targets, targets)

References

[1] C.H.Taal, R.C.Hendriks, R.Heusdens, J.Jensen ‘A Short-Time: Objective Intelligibility Measure for Time-Frequency Weighted Noisy Speech’, ICASSP 2010, Texas, Dallas.
[2] C.H.Taal, R.C.Hendriks, R.Heusdens, J.Jensen ‘An Algorithm for: Intelligibility Prediction of Time-Frequency Weighted Noisy Speech’, IEEE Transactions on Audio, Speech, and Language Processing, 2011.
[3] Jesper Jensen and Cees H. Taal, ‘An Algorithm for Predicting the: Intelligibility of Speech Masked by Modulated Noise Maskers’, IEEE Transactions on Audio, Speech and Language Processing, 2016.