TS-CGANet: A Two-Stage Complex and Real Dual-Path Sub-Band Fusion Network for Full-Band Speech Enhancement

Abstract

Speech enhancement based on deep neural networks faces difficulties, as modeling more frequency bands can lead to a decrease in the resolution of low-frequency bands and increase the computational complexity. Previously, we proposed a convolution-augmented gated attention unit (CGAU), which captured local and global correlation in speech signals through the fusion of the convolution and gated attention unit. In this paper, we further improved the CGAU, and proposed a two-stage complex and real dual-path sub-band fusion network for full-band speech enhancement called TS-CGANet. Specifically, we proposed a dual-path CGA network to enhance low-band (0–8 kHz) speech signals. In the medium band (8–16 kHz) and high band (16–24 kHz), noise suppression is only performed in the magnitude domain. The Voice Bank+DEMAND dataset was used to conduct experiments on the proposed TS-CGANet, which consistently outperformed state-of-the-art full-band baselines, as evidenced by the results.