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 aspip install torchmetrics[audio]
orpip install pystoi
Forward accepts
preds
:shape [...,time]
target
:shape [...,time]
- Parameters
- 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.
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 aspip install torchmetrics[audio]
orpip install pystoi
- Parameters
- Return type
- 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.