Noise-Aware Speech Separation with Contrastive Learning-Reference-Cited by-同舟云学术

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Noise-Aware Speech Separation with Contrastive Learning

Published:2024-04-14 Issue: Volume:44 Page:1381-1385
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Zhang Zizheng¹,Chen Chen²,Chen Hsin-Hung³,Liu Xiang¹,Hu Yuchen²,Chng Eng Siong²

Affiliation:

1. Peking University,School of Software and Microelectronics,China

2. Nanyang Technological University,School of Computer Science and Engineering,Singapore

3. Georgia Institute of Technology,School of Electrical and Computer Engineering,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10448214.pdf?arnumber=10448214

Reference20 articles.

1. The cocktail-party problem revisited: early processing and selection of multi-talker speech

2. Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation

3. Dual-Path RNN: Efficient Long Sequence Modeling for Time-Domain Single-Channel Speech Separation

4. Attention Is All You Need In Speech Separation

5. Monaural Speech Separation Using Dual-Output Deep Neural Network with Multiple Joint Constraint

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Novel Loss Incorporating Residual Signal Information for Target Speaker Extraction Under Low-SNR Conditions;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

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