Comparison of blind source separation algorithms

Li, Yan and Powers, David and Peach, James (2001) Comparison of blind source separation algorithms. In: WSES International Conference on Neural Networks and Applications 2001, 11-15 Feb 2001, Tenerife, Spain.

Abstract

A set of experiments are designed to evaluate and compare the performances of three well known blind source separation algorithms in this paper. The specific algorithms studied are two group of neural networks algorithms, Bell and Sejnowski's infomax algorithm and Hyvärinen's fixed-point family, and J. F. Cardoso's joint approxomate diagonalization of eigen-matrices algorithm. In this paper, the algorithms are quantitively evaluated and compared using the three measures, MATLAB flops (floating point operations), the difference between the mixing and separating matrices and the signal-to-noise ratios of the separated signals in this paper.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: FROM: Proceedings of the Conference on Neural Networks and Applications, Tenerife, Spain 2001
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 30 Nov 2007 11:35
Last Modified: 09 Apr 2018 00:31
Uncontrolled Keywords: blind souce separation; fixed-point algorithm; information maximisation; JADE
Fields of Research : 17 Psychology and Cognitive Sciences > 1702 Cognitive Sciences > 170205 Neurocognitive Patterns and Neural Networks
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
URI: http://eprints.usq.edu.au/id/eprint/9585

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