TY - JOUR

T1 - Blind source separation of more sources than mixtures using generalized exponential mixture models

AU - Shi, Zhenwei

AU - Tang, Huanwen

AU - Liu, Wenyu

AU - Tang, Yiyuan

N1 - Funding Information:
The authors would like to thank the editor Prof. R. Newcomb for his helpful suggestions. We are also grateful to all the anonymous reviewers who provided insightful and helpful comments. The work was supported by NSFC (30170321,90103033), MOE (KP0302) and MOST (2001CCA00700).

PY - 2004/10

Y1 - 2004/10

N2 - Blind source separation is discussed with more sources than mixtures in this paper. The blind separation technique includes two steps. The first step is to estimate a mixing matrix, and the second is to estimate sources. If the sources are sparse, the mixing matrix can be estimated by using the generalized exponential mixture model. The generalized exponential mixture model is a powerful uniform framework to learn the mixing matrix for sparse sources. A gradient learning algorithm for the generalized exponential mixture model is derived. After estimating the mixing matrix, the sources can be obtained by using the maximum a posteriori approach. The speech-signal experiments demonstrate effectiveness of the proposed approach.

AB - Blind source separation is discussed with more sources than mixtures in this paper. The blind separation technique includes two steps. The first step is to estimate a mixing matrix, and the second is to estimate sources. If the sources are sparse, the mixing matrix can be estimated by using the generalized exponential mixture model. The generalized exponential mixture model is a powerful uniform framework to learn the mixing matrix for sparse sources. A gradient learning algorithm for the generalized exponential mixture model is derived. After estimating the mixing matrix, the sources can be obtained by using the maximum a posteriori approach. The speech-signal experiments demonstrate effectiveness of the proposed approach.

KW - Blind source separation

KW - Generalized exponential mixture model

KW - Independent component analysis

KW - Overcomplete representation

UR - http://www.scopus.com/inward/record.url?scp=10244251597&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=10244251597&partnerID=8YFLogxK

U2 - 10.1016/j.neucom.2004.05.001

DO - 10.1016/j.neucom.2004.05.001

M3 - Article

AN - SCOPUS:10244251597

SN - 0925-2312

VL - 61

SP - 461

EP - 469

JO - Neurocomputing

JF - Neurocomputing

IS - 1-4

ER -