SESNet: A Speech Enhancement and Separation Network in Noisy Reverberant Environments

被引：0
|
作者：
Wang, Liusong [1 ,2 ]
Gao, Yuan [1 ,2 ]
Cao, Kaimin [1 ,2 ]
Hu, Ying [1 ,2 ]
机构：
[1] Xinjiang Univ, Sch Comp Sci & Technol, Urumqi, Peoples R China
[2] Key Lab Signal Detect & Proc Xinjiang, Urumqi, Peoples R China
来源：
MAN-MACHINE SPEECH COMMUNICATION, NCMMSC 2024 | 2025年 / 2312卷
关键词：
Speech enhancement; Speech separation; Noisy reverberant environment; Former block;
D O I：
10.1007/978-981-96-1045-7_4
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
Speech enhancement and separation in noisy reverberant environments are very challenging tasks. In this paper, we propose a speech enhancement and separation network, SESNet, for speech enhancement or speech separation in noisy reverberant environments, which is a multi-scale encoder-decoder architecture including a global-local feature extractor (GLFE). We also explored four kinds of Former blocks to be equipped in GLFE. We evaluate the performance of speech enhancement and speech separation on the VoiceBank+DEMAND and the WHAMR! datasets. The experimental results show that the SESNet has excellent performance for single- and multi-channel speech enhancement, and single-channel multi-speaker speech separation, keeping with a small model size.
引用
收藏
页码：44 / 54
页数：11
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