Blind source separation of more sources than mixtures using overcomplete representations

Empirical results were obtained for the blind source separation of more sources than mixtures using a previously proposed framework for learning overcomplete representations. This technique assumes a linear mixing model with additive noise and involves two steps: (1) learning an overcomplete represe...

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Bibliographic Details
Published inIEEE signal processing letters Vol. 6; no. 4; pp. 87 - 90
Main Authors Te-Won Lee, Lewicki, M.S., Girolami, M., Sejnowski, T.J.
Format Journal Article
LanguageEnglish
Published IEEE 01.04.1999
Subjects
Additive noise
Biology computing
Blind source separation
Blinds
Dictionaries
Independent component analysis
Laboratories
Learning
Mathematical models
Multiple signal classification
Noise
Principal component analysis
Representations
Separation
Signal processing
Speech
Speech analysis
Vectors
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Summary:Empirical results were obtained for the blind source separation of more sources than mixtures using a previously proposed framework for learning overcomplete representations. This technique assumes a linear mixing model with additive noise and involves two steps: (1) learning an overcomplete representation for the observed data and (2) inferring sources given a sparse prior on the coefficients. We demonstrate that three speech signals can be separated with good fidelity given only two mixtures of the three signals. Similar results were obtained with mixtures of two speech signals and one music signal.
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ISSN:1070-9908
1558-2361
DOI:10.1109/97.752062