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Neural Computing and Applications
Published in: Neural Computing and Applications 3-4/2003

01-12-2003 | Original Article

A simplified approach to independent component analysis

Authors: Yogesh Singh, C. S. Rai

Published in: Neural Computing and Applications | Issue 3-4/2003

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Abstract

Independent Component Analysis (ICA) is one of the fastest growing fields in the area of neural networks and signal processing. Blind Source Separation (BSS) is one of the applications of ICA. In this paper, ICA has been used for separating unknown source signals. BSS is used to extract independent signal components from their observed linear mixtures at an array of sensors. Various statistical techniques based on information theoretic and algebraic approaches exist for performing ICA. In this paper, we have used an objective function based on independence criterion of the signals. Optimisation of this objective function yields a neural algorithm along with a non-linear function for signal separation. Performance of the algorithm for artificially generated signals as well as audio signals has been evaluated.
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Metadata
Title
A simplified approach to independent component analysis
Authors
Yogesh Singh
C. S. Rai
Publication date
01-12-2003
Publisher
Springer-Verlag
Published in
Neural Computing and Applications / Issue 3-4/2003
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-003-0379-7

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