Speech Enhancement Performance Based on the MANNER Network Using Feature Fusion

Author:

Wang Shijie1,Li Ji2ORCID,Shao Lei2,Liu Hongli2,Zhu Lihua2,Zhu Xiaochen1

Affiliation:

1. School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China

2. Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control, Tianjin 300384, China

Abstract

The problems that the multi-view attention network for noise erasure (MANNER) cannot take into account are the local and global features in the speech enhancement of long sequences. An attention and feature fusion MANNER (AF-MANNER) network is proposed, which improves the multi-view attention (MA) module in MANNER and replaces the global and local attention in the module. AF-MANNER also designs the feature-weighted fusion module to fuse the features of flash attention and neighborhood attention to enhance the feature expression of the network. The final ablation studies show that this network exhibits a good performance in speech enhancement and that its structure is valuable for improving the intelligibility and perceptual quality of speech.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

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