A Hybrid Technique for Blind Separation of Non-Gaussian and Time-Correlated Sources Using a Multicomponent Approach

Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient fastICA (EFICA) offers an asymptotically optimal solution to this problem when all of the sources obey a generalized Gaussian distribution, at most...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2008-03, Vol.19 (3), p.421-430
Main Authors: Tichavsky, P., Koldovsky, Z., Yeredor, A., Gomez-Herrero, G., Doron, E.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Applied sciences
Artificial intelligence
Asymptotic properties
Blind source separation
Blinds
Brain modeling
Computer science
control theory
systems
Computer simulation
Connectionism. Neural networks
Educational programs
Electroencephalography
Exact sciences and technology
Gaussian
Gaussian distribution
Humans
Independent component analysis
independent component analysis (ICA)
Information theory
Models, Statistical
Neural Networks (Computer)
Non-Gaussian
Optimization
Signal processing
Signal processing algorithms
Signal Processing, Computer-Assisted
Source separation
Spectra
Time
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Summary:Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient fastICA (EFICA) offers an asymptotically optimal solution to this problem when all of the sources obey a generalized Gaussian distribution, at most one of them is Gaussian, and each is independent and identically distributed (i.i.d.) in time. Likewise, weights-adjusted second-order blind identification (WASOBI) is asymptotically optimal when all the sources are Gaussian and can be modeled as autoregressive (AR) processes with distinct spectra. Nevertheless, real-life mixtures are likely to contain both Gaussian AR and non-Gaussian i.i.d. sources, rendering WASOBI and EFICA severely suboptimal. In this paper, we propose a novel scheme for combining the strengths of EFICA and WASOBI in order to deal with such hybrid mixtures. Simulations show that our approach outperforms competing algorithms designed for separating similar mixtures.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/TNN.2007.908648