Real-Time Multichannel Speech Separation and Enhancement Using a Beamspace-Domain-Based Lightweight CNN-Reference-Cited by-同舟云学术

Real-Time Multichannel Speech Separation and Enhancement Using a Beamspace-Domain-Based Lightweight CNN

Published:2023-06-04 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Olivieri Marco¹,Comanducci Luca¹,Pezzoli Mirco¹,Balsarri Davide²,Menescardi Luca²,Buccoli Michele²,Pecorino Simone²,Grosso Antonio²,Antonacci Fabio¹,Sarti Augusto¹

Affiliation:

1. Informazione e Bioingegneria - Politecnico di Milano,Dipartimento di Elettronica,Milan,Italy

2. BdSound S.r.l,Milan,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10094559/10094560/10096891.pdf?arnumber=10096891

Reference28 articles.

1. On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments

2. Enhancing End-to-End Multi-Channel Speech Separation Via Spatial Feature Learning

3. Embedding and Beamforming: All-Neural Causal Beamformer for Multichannel Speech Enhancement

4. Implicit Neural Spatial Filtering for Multichannel Source Separation in the Waveform Domain

5. ADL-MVDR: All Deep Learning MVDR Beamformer for Target Speech Separation

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