Multi-Scale Temporal Frequency Convolutional Network With Axial Attention for Speech Enhancement
Guochang Zhang, Libiao Yu, Chunliang Wang, Jianqiang Wei
Abstract
Speech quality is often degraded by acoustic echoes, background noise, and reverberation. In this paper, we propose a system consisting of deep learning and signal processing to simultaneously suppress echoes, noise, and reverberation. For the deep learning, we design a novel speech dense-prediction backbone. For the signal processing, a linear acoustic echo canceller is used as conditional information for deep learning. To improve the performance of the speech dense-prediction backbone, strategies such as a microphone and reference phase encoder, multi-scale time-frequency processing, and streaming axial attention are designed. The proposed system ranked first in both AEC and DNS Challenge (non-personal track) of ICASSP 2022. In addition, this backbone has also been extended to the multi-channel speech enhancement task, and placed second in ICASSP 2022 L3DAS22 Challenge <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .