Blind sparse source separation using cluster particle swarm optimization technique

Authors:
Chan-Cheng Liu;Tsung-Ying Sun;Kan-Yuan Li;Sheng-Ta Hsieh;Shang-Jeng Tsai
Affiliations:
Intelligent Signal Processing Lab., Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan, R.O.C.;Intelligent Signal Processing Lab., Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan, R.O.C.;Intelligent Signal Processing Lab., Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan, R.O.C.;Intelligent Signal Processing Lab., Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan, R.O.C.;Intelligent Signal Processing Lab., Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan, R.O.C.
Venue:
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Year:
2007

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Abstract

In this paper, a source number and mixing matrix identifications with Particle Swarm Optimization (PSO) are proposed for blind sparse source separation (BSS) problem which involves more sources than sensors (i.e. under-determined) and the assumption of unknown source number. We regard each particle of PSO as a probable set of mixing vectors, and modify the global item of the conventional velocity updating equation by a cluster center. After particles optimized, the existing base vectors can be extracted from the optimal particle by the proposed adaptive threshold. Then, all source signals could be recovered correctly and precisely. Validation and effectualness of the proposed algorithm are demonstrated by computer simulation examples, and its performance is compared with some existing algorithms.