MANNER: Multi-View Attention Network For Noise Erasure
Hyun Joon Park, Byung Ha Kang, Wooseok Shin, Jin Sob Kim, Sung Won Han
Abstract
In the field of speech enhancement, time domain methods have difficulties in achieving both high performance and efficiency. Recently, dual-path models have been adopted to represent long sequential features, but they still have limited representations and poor memory efficiency. In this study, we propose Multi-view Attention Network for Noise ERasure (MANNER) consisting of a convolutional encoder-decoder with a multi-view attention block, applied to the time-domain signals. MANNER efficiently extracts three different representations from noisy speech and estimates high-quality clean speech. We evaluated MANNER on the VoiceBank-DEMAND dataset in terms of five objective speech quality metrics. Experimental results show that MAN-NER achieves state-of-the-art performance while efficiently processing noisy speech.
BibTeX
@inproceedings{icassp2022_mannermultiviewa,
title = {MANNER: Multi-View Attention Network For Noise Erasure},
author = {Hyun Joon Park and Byung Ha Kang and Wooseok Shin and Jin Sob Kim and Sung Won Han},
booktitle = {ICASSP 2022},
year = {2022}
}