Optimizing Time Domain Fully Convolutional Networks for 3D Speech Enhancement in a Reverberant Environment Using Perceptual Losses

Noise in 3D reverberant environment is detrimental to several downstream applications. In this work, we propose a novel approach to 3D speech enhancement directly in the time domain through the usage of Fully Convolutional Networks (FCN) with a custom loss function based on the combination of a perceptual loss, built on top of the wav2vec model and a soft version of the short-time objective intelligibility (STOI) metric. The dataset and experiments were based on Task 1 of the L3DAS21 challenge. Our model achieves a STOI score of 0.82, word error rate (WER) equal to 0.36, and a score of 0.73 in the metric proposed by the challenge based on STOI and WER combination using as reference the development set. Our submission, based on this method, was ranked second in Task 1 of the L3DAS21 challenge.