MULTI-TASK SUB-BAND NETWORK FOR DEEP RESIDUAL ECHO SUPPRESSION
Jiayao Sun (Northwestern Polytechnical University); Dawei Luo (Li Auto); Zhaoxia LI (Li Auto); Jingdong Li (Tencent ); Yukai Jv (Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, School of Computer Science, Northwestern Polytechnical University); Yang Li (Li Auto)
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This paper introduces the SWANT team’s entry to the ICASSP 2023
AEC Challenge. We submit a system that cascades a linear filter
with a neural post-filter. Particularly, we adopt sub-band process-
ing to handle full-band signal and shape the network with multi-task
learning, where dual signal voice activity detection (DSVAD) and
echo estimation are adopted as auxiliary tasks. Moreover, we par-
ticularly improve the time frequency convolution module (TFCM)
to increase the receptive field using small convolution kernels. Fi-
nally, our system has ranked 4th in ICASSP 2023 AEC Challenge
Non-personalized track.