Renet: A Time-Frequency Domain General Speech Restoration Network for Icassp 2024 Speech Signal Improvement Challenge

The ICASSP 2024 Speech Signal Improvement (SSI) Challenge seeks to address speech quality degradation problems in telecommunication systems. In this context, this paper proposes RENet, a time-frequency (T-F) domain method leveraging complex spectrum mapping to mitigate speech distortions. Specifically, the proposed RENet is a multi-stage network. First, TF-GridGAN was designed to recover the degraded speech with a generative adversarial network (GAN). Second, a full-band enhancement module was introduced to eliminate residual noises and artifacts existed in the output of TF-GridGAN. Finally, a lightweight bandwidth extension (BWE) network was implemented to further improve the speech quality by generating high-resolution speeches. Subjective results confirmed the competitive performance of the proposed method under various distortions, and the proposed method ranked the 2nd place in the non-real-time track of the ICASSP 2024 SSI Challenge.