Spectro-Temporal Attention Based Voice Activity Detection (pytorch)

   

Pytorch implementation of "Spectro-Temporal Attention-based Voice Activity Detection"

My implementation of STAM provides slightly better performance compared to the original tensorflow one:

Tensorflow: Global AUC: 99.86, F1-score: 98.15, DCF: 1.32, accuracy: 97.90, precision: 99.10

Pytorch: Global AUC: 99.87, F1-score: 98.31, DCF: 1.18, accuracy: 98.07, precision: 99.06

Training data

TIMIT training data + NOISEX (SNR: -10, -5, 0, 5, 10dB)

Testing data

TIMIT testing data + AURORA (SNR: -5, 0, 5, 10dB)

Reference

https://github.com/ByeonghakKim/ispl-speech/tree/05b5a175edf2c312db398bad82a3b3cc56da4756/Voice%20activity%20detection