An ICA-Based Method for Blind Source Separation in Sparse Domains

Authors:
Everton Z. Nadalin;Ricardo Suyama;Romis Attux
Affiliations:
Departament of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering, University of Campinas (Unicamp), Campinas, Brazil CEP 13083-970 and Lab. of Signal Pro ...;Departament of Microwave and Optics School of Electrical and Computer Engineering, University of Campinas (Unicamp), Campinas, Brazil CEP 13083-970 and Lab. of Signal Processing for Communications ...;Departament of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering, University of Campinas (Unicamp), Campinas, Brazil CEP 13083-970 and Lab. of Signal Pro ...
Venue:
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Year:
2009

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Abstract

In this work, we propose and analyze a method to solve the problem of underdetermined blind source separation (and identification) that employs the ideas of sparse component analysis (SCA) and independent component analysis (ICA). The main rationale of the approach is to allow the possibility of reaching a method that is more robust with respect to the degree of sparseness of the involved signals and more effective in the use of information brought by multiple sensors. The ICA-based solution is tested with the aid of three representative scenarios and its performance is compared with that of one of the soundest SCA techniques available, the DEMIXN algorithm.