A Cross-channel Attention-based Wave-U-Net for Multi-channel Speech Enhancement

International Conference
2020-10-01 16:50
Authors : Minh-Tri Ho, Jinyoung Lee, Bong-Ki Lee, Dong Hoon Yi, Hong-Goo Kang

Year : 2020

Publisher / Conference : INTERSPEECH

Related project : 극심한 잡음 환경에서의 음성 인식 성능향상을 위한 다채널 음질 개선 알고리즘 개발

Presentation : 구두

In this paper, we present a novel architecture for multi-channel speech enhancement using a cross-channel attention-based Wave-U-Net structure. Despite the advantages of utilizing spatial information as well as spectral information, it is challenging to effectively train a multi-channel deep learning system in an end-to-end framework.
With a channel-independent encoding architecture for spectral estimation and a strategy to extract spatial information through an inter-channel attention mechanism, we implement a multi-channel speech enhancement system that has high performance even in reverberant and extremely noisy environments.
Experimental results show that the proposed architecture has superior performance in terms of signal-to-distortion ratio improvement (SDRi), short-time objective intelligence (STOI), and phoneme error rate (PER) for speech recognition.
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