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Short-Time Objective Intelligibility (STOI)

Module Interface

class torchmetrics.audio.stoi.ShortTimeObjectiveIntelligibility(fs, extended=False, **kwargs)[source]

STOI (Short-Time Objective Intelligibility, see [2,3]), a wrapper for the pystoi package [1]. Note that input will be moved to cpu to perform the metric calculation.

Intelligibility measure which is highly correlated with the intelligibility of degraded speech signals, e.g., due to additive noise, single-/multi-channel noise reduction, binary masking and vocoded speech as in CI simulations. The STOI-measure is intrusive, i.e., a function of the clean and degraded speech signals. STOI may be a good alternative to the speech intelligibility index (SII) or the speech transmission index (STI), when you are interested in the effect of nonlinear processing to noisy speech, e.g., noise reduction, binary masking algorithms, on speech intelligibility. Description taken from Cees Taal’s website.

Note

using this metrics requires you to have pystoi install. Either install as pip install torchmetrics[audio] or pip install pystoi

Forward accepts

  • preds: shape [...,time]

  • target: shape [...,time]

Parameters
  • fs (int) – sampling frequency (Hz)

  • extended (bool) – whether to use the extended STOI described in [4]

  • kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Returns

average STOI value

Raises

ModuleNotFoundError – If pystoi package is not installed

Example

>>> from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility
>>> import torch
>>> g = torch.manual_seed(1)
>>> preds = torch.randn(8000)
>>> target = torch.randn(8000)
>>> stoi = ShortTimeObjectiveIntelligibility(8000, False)
>>> stoi(preds, target)
tensor(-0.0100)

References

[1] https://github.com/mpariente/pystoi

[2] 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.

[3] 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.

[4] J. Jensen and C. H. Taal, ‘An Algorithm for Predicting the Intelligibility of Speech Masked by Modulated Noise Maskers’, IEEE Transactions on Audio, Speech and Language Processing, 2016.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Computes average STOI.

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters
  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

Return type

None

Functional Interface

torchmetrics.functional.audio.stoi.short_time_objective_intelligibility(preds, target, fs, extended=False, keep_same_device=False)[source]

STOI (Short-Time Objective Intelligibility, see [2,3]), a wrapper for the pystoi package [1]. Note that input will be moved to cpu to perform the metric calculation.

Intelligibility measure which is highly correlated with the intelligibility of degraded speech signals, e.g., due to additive noise, single/multi-channel noise reduction, binary masking and vocoded speech as in CI simulations. The STOI-measure is intrusive, i.e., a function of the clean and degraded speech signals. STOI may be a good alternative to the speech intelligibility index (SII) or the speech transmission index (STI), when you are interested in the effect of nonlinear processing to noisy speech, e.g., noise reduction, binary masking algorithms, on speech intelligibility. Description taken from Cees Taal’s website.

Note

using this metrics requires you to have pystoi install. Either install as pip install torchmetrics[audio] or pip install pystoi

Parameters
  • preds (Tensor) – shape [..., time]

  • target (Tensor) – shape [..., time]

  • fs (int) – sampling frequency (Hz)

  • extended (bool) – whether to use the extended STOI described in [4]

  • keep_same_device (bool) – whether to move the stoi value to the device of preds

Return type

Tensor

Returns

stoi value of shape […]

Raises

ModuleNotFoundError – If pystoi package is not installed

Example

>>> from torchmetrics.functional.audio.stoi import short_time_objective_intelligibility
>>> import torch
>>> g = torch.manual_seed(1)
>>> preds = torch.randn(8000)
>>> target = torch.randn(8000)
>>> short_time_objective_intelligibility(preds, target, 8000).float()
tensor(-0.0100)

References

[1] https://github.com/mpariente/pystoi

[2] 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.

[3] 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.

[4] J. Jensen and C. H. Taal, ‘An Algorithm for Predicting the Intelligibility of Speech Masked by Modulated Noise Maskers’, IEEE Transactions on Audio, Speech and Language Processing, 2016.

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