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

Zhenwei Shi; Huanwen Tang; Yiyuan Tang

doi:10.1016/j.patrec.2005.05.006

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

Zhenwei Shi^*
, Huanwen Tang
, Yiyuan Tang

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

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.

Original language	English
Pages (from-to)	2491-2499
Number of pages	9
Journal	Pattern Recognition Letters
Volume	26
Issue number	16
DOIs	https://doi.org/10.1016/j.patrec.2005.05.006
State	Published - Dec 2005
Externally published	Yes

Keywords

Blind source separation
Independent component analysis
Overcomplete representation
Signal processing
Sparse mixture model

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10.1016/j.patrec.2005.05.006

Cite this

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title = "Blind source separation of more sources than mixtures using sparse mixture models",

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.",

keywords = "Blind source separation, Independent component analysis, Overcomplete representation, Signal processing, Sparse mixture model",

author = "Zhenwei Shi and Huanwen Tang and Yiyuan Tang",

year = "2005",

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doi = "10.1016/j.patrec.2005.05.006",

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AU - Tang, Huanwen

AU - Tang, Yiyuan

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

UR - https://www.scopus.com/pages/publications/27644559088

U2 - 10.1016/j.patrec.2005.05.006

DO - 10.1016/j.patrec.2005.05.006

M3 - 文章

AN - SCOPUS:27644559088

SN - 0167-8655

VL - 26

SP - 2491

EP - 2499

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

IS - 16

ER -

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

Abstract

Keywords

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