A sparsity-relaxed algorithm for the under-determined convolutive blind source separation

Junjie Yang; Yi Guo; Zuyuan Yang; Chao Yang

doi:10.1117/12.2538077

27 November 2019 A sparsity-relaxed algorithm for the under-determined convolutive blind source separation

Junjie Yang, Yi Guo, Zuyuan Yang, Chao Yang

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113212I (2019) https://doi.org/10.1117/12.2538077
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

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.

Citation Download Citation

Junjie Yang, Yi Guo, Zuyuan Yang, and Chao Yang "A sparsity-relaxed algorithm for the under-determined convolutive blind source separation", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113212I (27 November 2019); https://doi.org/10.1117/12.2538077

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KEYWORDS

Signal processing

Optimization (mathematics)

Systems modeling

Time-frequency analysis

Biomedical optics

Distortion

Matrices

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