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

pip install torch_stoi

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 🚀

import torch
from torch import nn
from torch_stoi import NegSTOILoss
sample_rate = 16000
loss_func = NegSTOILoss(sample_rate=sample_rate)
# Your nnet and optimizer definition here
nnet = nn.Module()
noisy_speech = torch.randn(2, 16000)
clean_speech = torch.randn(2, 16000)
# Estimate clean speech
est_speech = nnet(noisy_speech)
# Compute loss and backward (then step etc...)
loss_batch = loss_func(est_speech, clean_speech)
loss_batch.mean().backward()

Values obtained with the NumPy version are compared to the PyTorch version in the following graphs.

Classic STOI measure

Extended STOI measure

Classic STOI measure

Extended STOI measure

16kHz signals used to compare both versions contained a lot of silence, which explains why the match is very bad without VAD.

Coming in the near future

• [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.