Blind source separation of more sources than mixtures using generalized exponential mixture models
Shi, ZW; Tang, HW; Liu, WY; Tang, YY
2004-10-01
发表期刊NEUROCOMPUTING
ISSN0925-2312
文章类型Article
卷号61页码:461-469
摘要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. (C) 2004 Elsevier B.V. All rights reserved.
关键词independent component analysis blind source separation overcomplete representation generalized exponential mixture model
收录类别SCI
语种英语
WOS记录号WOS:000224511500030
引用统计
被引频次:9[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.psych.ac.cn/handle/311026/13930
专题中国科学院心理研究所回溯数据库(1956-2010)
作者单位1.Dalian Univ Technol, Inst Neuroinformat, Dalian 116023, Peoples R China
2.Dalian Univ Technol, Inst Computat Biol & Bioinformat, Dalian 116023, Peoples R China
3.Chinese Acad Sci, Lab Visual Informat Proc, Beijing 100101, Peoples R China
4.Chinese Acad Sci, Key Lab Mental Hlth, Beijing 100101, Peoples R China
5.Dalian Univ Technol, Dept Foreign Languages, Dalian 116023, Peoples R China
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Shi, ZW,Tang, HW,Liu, WY,et al. Blind source separation of more sources than mixtures using generalized exponential mixture models[J]. NEUROCOMPUTING,2004,61:461-469.
APA Shi, ZW,Tang, HW,Liu, WY,&Tang, YY.(2004).Blind source separation of more sources than mixtures using generalized exponential mixture models.NEUROCOMPUTING,61,461-469.
MLA Shi, ZW,et al."Blind source separation of more sources than mixtures using generalized exponential mixture models".NEUROCOMPUTING 61(2004):461-469.
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