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KS-Net: Multi-band joint speech restoration and enhancement network for 2024 ICASSP SSI Challenge

Authors :
Yu, Guochen
Han, Runqiang
Xu, Chenglin
Zhao, Haoran
Li, Nan
Zhang, Chen
Zheng, Xiguang
Zhou, Chao
Huang, Qi
Yu, Bing
Publication Year :
2024

Abstract

This paper presents the speech restoration and enhancement system created by the 1024K team for the ICASSP 2024 Speech Signal Improvement (SSI) Challenge. Our system consists of a generative adversarial network (GAN) in complex-domain for speech restoration and a fine-grained multi-band fusion module for speech enhancement. In the blind test set of SSI, the proposed system achieves an overall mean opinion score (MOS) of 3.49 based on ITU-T P.804 and a Word Accuracy Rate (WAcc) of 0.78 for the real-time track, as well as an overall P.804 MOS of 3.43 and a WAcc of 0.78 for the non-real-time track, ranking 1st in both tracks.<br />Comment: Accepted to ICASSP 2024; Rank 1st in ICASSP 2024 Speech Signal Improvement (SSI) Challenge

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2402.01808
Document Type :
Working Paper