torch-stoi 0.0.1

Computes Short Term Objective Intelligibility in PyTorch

PyTorch implementation of STOI

Implementation of the classical and extended Short Term Objective Intelligibility in PyTorch. See also Cees Taal's website and the python implementation

Important warning

This implementation is intended to be used as a loss function only.
It doesn't replicate the exact behavior of the original metrics but the results should be close enough that it can be used as a loss function. See the Notes in the NegSTOILoss class.

Quantitative comparison coming soon hopefully :rocket:

Install

Ontoit

Usage

Ontoit

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] 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.