Flexible ICA in Complex and Nonlinear Environment by Mutual Information Minimization

Author

Vigliano, Daniele ; Scarpiniti, Michele ; Parisi, Raffaele ; Uncini, Aurelio

Author_Institution

INFOCOM Dept., Univ. degli Studi di Roma "La Sapienza", Rome

fYear

2006

fDate

6-8 Sept. 2006

Firstpage

59

Lastpage

63

Abstract

This paper introduces an Independent Component Analysis (ICA) approach to the separation of nonlinear mixtures in the complex domain. Source separation is performed by the minimization of output mutual information (MMI approach). Nonlinear complex functions involved in the processing are realized by the so called "splitting functions" which work on the real and the imaginary part of the signal respectively. Some experimental results that demonstrate the effectiveness of the proposed method are shown.

Keywords

independent component analysis; large-scale systems; minimisation; nonlinear systems; source separation; complex domain; complex environment; flexible ICA; independent component analysis; mutual information minimization; nonlinear complex functions; nonlinear environment; nonlinear mixtures; output mutual information; source separation; splitting functions; Biomedical signal processing; Blind source separation; Frequency domain analysis; Independent component analysis; Mutual information; Nonlinear distortion; Signal processing; Signal processing algorithms; Source separation; Virtual reality;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on

Conference_Location

Arlington, VA

ISSN

1551-2541

Print_ISBN

1-4244-0656-0

Electronic_ISBN

1551-2541

Type

conf

DOI

10.1109/MLSP.2006.275522

Filename

4053621