Investigation of Network Architecture for Single-Channel End-to-End Denoising

Abstract

This paper examines the effectiveness of a fully convolutional time-domain audio separation network (Conv-TasNet) on single-channel denoising. Conv-TasNet, which has a structure to explicitly estimate a mask for encoded features, has shown to be effective in single-channel sound source separation in noise-free environments, but it has not been applied to denoising. Therefore, the present study investigates a method of learning Conv-TasNet for denoising and clarifies the optimal structure for single-channel end-to-end modeling. Experimental comparisons conducted using the CHiME-3 dataset demonstrate that Conv-TasNet performs well in denoising and yields improvements in single-channel end-to-end denoising over existing denoising autoencoder-based modeling.