Methods for the blind signal separation problem
Paper
Paper/Presentation Title | Methods for the blind signal separation problem |
---|---|
Presentation Type | Paper |
Authors | Li, Yan (Author), Wen, Peng (Author) and Powers, David (Author) |
Journal or Proceedings Title | Proceedings of the IEEE International Conference on Neural Networks and Signal Processing (ICNNSP 2003) |
Journal Citation | 2, pp. 1386-1389 |
Number of Pages | 4 |
Year | 2003 |
Place of Publication | Piscataway, NJ. United States |
ISBN | 978-078037702-8 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICNNSP.2003.1281131 |
Web Address (URL) of Paper | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1281131 |
Conference/Event | ICNNSP 2003: International Conference on Neural Networks and Signal Processing |
Event Details | ICNNSP 2003: International Conference on Neural Networks and Signal Processing Event Date 14 to end of 17 Dec 2003 Event Location Nanjing, China |
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 |
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 |
ANZSRC Field of Research 2020 | 400607. Signal processing |
469999. Other information and computing sciences not elsewhere classified | |
461399. Theory of computation not elsewhere classified | |
Public Notes | © 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. |
Byline Affiliations | Department of Mathematics and Computing |
Department of Electrical, Electronic and Computer Engineering | |
Flinders University |
https://research.usq.edu.au/item/9zy56/methods-for-the-blind-signal-separation-problem
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