Methods for the blind signal separation problem

Li, Yan and Wen, Peng and Powers, David (2003) Methods for the blind signal separation problem. In: ICNNSP 2003: International Conference on Neural Networks and Signal Processing , 14-17 Dec 2003 , Nanjing, China.

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Abstract

This paper classifies and reviews the available algorithms to blind signal separation (BSS) problem. Based on the separation criteria, we broadly divide all the reviewed algorithms into four categories, namely: classical adaptive, higher-order statistics based, information theory based algorithms and others. For algorithms which might fall into more than one category, categorizing is made according to their main features. Most of the algorithms reviewed in this paper are benchmarks in BSS area. Many BSS algorithms use neural networks to perform the learning rules, probably because neural networks are powerful in nonlinear mapping and learning ability


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2003 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Faculty/School / Institute/Centre: Historic - Faculty of Engineering and Surveying - Department of Electrical, Electronic and Computer Engineering (Up to 30 Jun 2013)
Faculty/School / Institute/Centre: Historic - Faculty of Engineering and Surveying - Department of Electrical, Electronic and Computer Engineering (Up to 30 Jun 2013)
Date Deposited: 21 May 2010 09:27
Last Modified: 02 Jul 2013 23:55
Uncontrolled Keywords: adaptive filters; blind source separation; higher order statistics; independent component analysis; information theory; neural nets; adaptive algorithms; blind signal separation; higher order statistics based algorithms; information theory based algorithms; learning; neural networks; nonlinear mapping; separation criteria
Fields of Research (2008): 09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
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
Fields of Research (2020): 40 ENGINEERING > 4006 Communications engineering > 400607 Signal processing
46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4613 Theory of computation > 461399 Theory of computation not elsewhere classified
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Identification Number or DOI: https://doi.org/10.1109/ICNNSP.2003.1281131
URI: http://eprints.usq.edu.au/id/eprint/8165

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