Multi-channel speech enhancement using time-domain convolutional denoising autoencoder

This paper investigates the use of time-domain convolutional denoising autoencoders (TCDAEs) with multiple channels as a method of speech enhancement. In general, denoising autoencoders (DAEs), deep learning systems that map noise-corrupted into clean waveforms, have been shown to generate high-quality signals while working in the time domain without the intermediate stage of phase modeling. Convolutional DAEs are one of the popular structures which learns a mapping between noise-corrupted and clean waveforms with convolutional denoising autoencoder. Multi-channel signals for TCDAEs are promising because the different times of arrival of a signal can be directly processed with their convolutional structure, Up to this time, TCDAEs have only been applied to single-channel signals. This paper explorers the effectiveness of TCDAEs in a multichannel configuration. A multi-channel TCDAEs are evaluated on multi-channel speech enhancement experiments, yielding significant improvement over single-channel DAEs in terms of signal-to-distortion ratio, perceptual evaluation of speech quality (PESQ), and word error rate.