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Proceedings Paper

A sparsity-relaxed algorithm for the under-determined convolutive blind source separation
Author(s): Junjie Yang; Yi Guo; Zuyuan Yang; Chao Yang
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Paper Abstract

Convolutive blind source separation (CBSS) is a kind of signal processing method by separating multiple sources from a convolutive mixing model. The concept of CBSS is to recover the latent sources in a reverberant environment. Usually, a two-stage scheme including the mixing matrix estimation and the source recovery are proposed to fulfill this target. In this paper, we mainly discuss the source recovery problem based on the knowledge of estimated mixing matrix. Specifically, this problem can be categorized as a sparse source construction optimization model, especially for the under-determined case where the number of sources is greater than the number of microphones. Inspirited by the fact that only few source components are active at each time-frequency slot, a new augmented Lagrange method is proposed to find the optimal sparse solution of sources with the ℓp norm (0<p<1) based measurement function. The proposed method relaxes the strict sparse assumption on sources, hence improve the source separation performance. The experiment results demonstrate that the proposed algorithm is superior than the state-of-the-art methods.

Paper Details

Date Published: 27 November 2019
PDF: 5 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113212I (27 November 2019); doi: 10.1117/12.2538077
Show Author Affiliations
Junjie Yang, Guangdong Univ. of Technology (China)
Yi Guo, Western Sydney Univ. (Australia)
Zuyuan Yang, Guangdong Univ. of Technology (China)
Chao Yang, Guangdong Univ. of Technology (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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