TY - JOUR
T1 - Blind source separation of more sources than mixtures using sparse mixture models
AU - Shi, Zhenwei
AU - Tang, Huanwen
AU - Tang, Yiyuan
N1 - Funding Information:
We are grateful to all the anonymous reviewers who provided insightful and helpful comments. The work was supported by NSFC (60472017, 90103033), MOE (KP0302) and MOST (ICPDF2003).
PY - 2005/12
Y1 - 2005/12
N2 - 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.
AB - 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.
KW - Blind source separation
KW - Independent component analysis
KW - Overcomplete representation
KW - Signal processing
KW - Sparse mixture model
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U2 - 10.1016/j.patrec.2005.05.006
DO - 10.1016/j.patrec.2005.05.006
M3 - Article
AN - SCOPUS:27644559088
SN - 0167-8655
VL - 26
SP - 2491
EP - 2499
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
IS - 16
ER -