Two-step Band-split Neural Network Approach for Full-band Residual Echo Suppression
Audio and Speech Processing
2023-03-14 v1
Abstract
This paper describes a Two-step Band-split Neural Network (TBNN) approach for full-band acoustic echo cancellation. Specifically, after linear filtering, we split the full-band signal into wide-band (16KHz) and high-band (16-48KHz) for residual echo removal with lower modeling difficulty. The wide-band signal is processed by an updated gated convolutional recurrent network (GCRN) with U encoder while the high-band signal is processed by a high-band post-filter net with lower complexity. Our approach submitted to ICASSP 2023 AEC Challenge has achieved an overall mean opinion score (MOS) of 4.344 and a word accuracy (WAcc) ratio of 0.795, leading to the 2 (tied) in the ranking of the non-personalized track.
Cite
@article{arxiv.2303.06828,
title = {Two-step Band-split Neural Network Approach for Full-band Residual Echo Suppression},
author = {Zihan Zhang and Shimin Zhang and Mingshuai Liu and Yanhong Leng and Zhe Han and Li Chen and Lei Xie},
journal= {arXiv preprint arXiv:2303.06828},
year = {2023}
}
Comments
Accepted by ICASSP 2023