Blind source separation of more sources than mixtures using sparse mixture models

Authors:
Zhenwei Shi;Huanwen Tang;Yiyuan Tang
Affiliations:
State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing 100084, PR China;Institute of Computational Biology and Bioinformatics, Dalian University of Technology, Dalian 116023, PR China;Institute of Neuroinformatics, Dalian University of Technology, Dalian 116023, PR China and Laboratory of Visual Information Processing, The Chinese Academy of Sciences, Beijing 100101, PR China a ...
Venue:
Pattern Recognition Letters
Year:
2005

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Abstract

In this paper, blind source separation is discussed with more sources than mixtures. This blind separation technique assumes a linear mixing model and involves two steps: (1) learning the mixing matrix for the observed data using the sparse mixture model and (2) inferring the sources by solving a linear programming problem after the mixing matrix is estimated. Through the experiments of the speech signals, we demonstrate the efficacy of this proposed approach.