Lite-RTSE: Exploring a Cost-Effective Lite DNN Model for Real-Time Speech Enhancement in RTC Scenarios-Reference-Cited by-同舟云学术

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Lite-RTSE: Exploring a Cost-Effective Lite DNN Model for Real-Time Speech Enhancement in RTC Scenarios

Published:2023 Issue: Volume:30 Page:1697-1701
ISSN:1070-9908
Container-title:IEEE Signal Processing Letters
language:
Short-container-title:IEEE Signal Process. Lett.

Author:

Liang Xingwei¹^ORCID,Zhang Lu²^ORCID,Wu Zhiyong³^ORCID,Xu Ruifeng⁴^ORCID

Affiliation:

1. Harbin Institute of Technology and Konka Group Company, Ltd., Shenzhen, China

2. Harbin Institute of Technology, Shenzhen, China

3. Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

4. Harbin Institute of Technology, Peng Cheng Laboratory, and Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Shenzhen, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

Shenzhen Foundational Research

Major Key Project of PCL

Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/97/10036333/10308952.pdf?arnumber=10308952

Reference42 articles.

1. A Wavenet for Speech Denoising

2. UNetGAN: A Robust Speech Enhancement Approach in Time Domain for Extremely Low Signal-to-Noise Ratio Condition

3. Long short-term memory for speaker generalization in supervised speech separation

4. Speech Enhancement with Fullband-Subband Cross-Attention Network

5. FullSubNet+: Channel Attention Fullsubnet with Complex Spectrograms for Speech Enhancement

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