Open Access

A Nonlinear Prediction Approach to the Blind Separation of Convolutive Mixtures

  • Ricardo Suyama1Email author,
  • Leonardo Tomazeli Duarte1,
  • Rafael Ferrari1,
  • Leandro Elias Paiva Rangel1,
  • Romis Ribeirode Faissol Attux1,
  • Charles Casimiro Cavalcante2,
  • Fernando José Von Zuben3 and
  • João Marcos Travassos Romano1
EURASIP Journal on Advances in Signal Processing20062007:043860

https://doi.org/10.1155/2007/43860

Received: 1 October 2005

Accepted: 11 June 2006

Published: 24 September 2006

Abstract

We propose a method for source separation of convolutive mixture based on nonlinear prediction-error filters. This approach converts the original problem into an instantaneous mixture problem, which can be solved by any of the several existing methods in the literature. We employ fuzzy filters to implement the prediction-error filter, and the efficacy of the proposed method is illustrated by some examples.

Keywords

Information TechnologyQuantum InformationOriginal ProblemSource SeparationPrediction Approach

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Authors’ Affiliations

(1)
Laboratory of Signal Processing for Communications (DSPCOM), School of Electrical and Computer Engineering, University of Campinas (Unicamp), CEP, Campinas, Brazil
(2)
Wireless Telecommunications Research Group (GTEL), Federal University of Ceará (UFC), CEP, Fortaleza, Brazil
(3)
Laboratory of Bioinformatics and Bio-inspired Computing (LBiC), School of Electrical and Computer Engineering, University of Campinas (Unicamp), CEP, Campinas, Brazil

References

  1. Hyvärinen A, Karhunen J, Oja E: Independent Component Analysis. John Wiley & Sons, New York, NY, USA; 2001.View ArticleGoogle Scholar
  2. Hérault J, Jutten C, Ans B: Détection de grandeurs primitives dans un message composite par une architecture de calcul neuromimétique en apprentissage non supervise. Actes du Xème Colloque (GRETSI '85), 1985, Nice, France 1017-1022.Google Scholar
  3. Cichocki A, Amari S: Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. John Wiley & Sons, New York, NY, USA; 2002.View ArticleGoogle Scholar
  4. Haykin S (Ed): Unsupervised Adaptive Filtering, Vol. I: Blind Source Separation. John Wiley & Sons, New York, NY, USA; 2000.Google Scholar
  5. Papadias CB: Unsupervised receiver processing techniques for linear space-time equalization of wideband multiple input/multiple output channels. IEEE Transactions on Signal Processing 2004,52(2):472-482. 10.1109/TSP.2003.821108MathSciNetView ArticleGoogle Scholar
  6. Castella M, Pesquet J-C, Petropulu AP: A family of frequency- and time-domain contrasts for blind separation of convolutive mixtures of temporally dependent signals. IEEE Transactions on Signal Processing 2005,53(1):107-120.MathSciNetView ArticleGoogle Scholar
  7. Comon P, Rota L: Blind separation of independent sources from convolutive mixtures. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 2003,E86-A(3):542-549.Google Scholar
  8. Simon C, Loubaton P, Jutten C: Separation of a class of convolutive mixtures: a contrast function approach. Signal Processing 2001,81(4):883-887. 10.1016/S0165-1684(00)00240-1View ArticleMATHGoogle Scholar
  9. Moreau E, Pesquet J-C: Generalized contrasts for multichannel blind deconvolution of linear systems. IEEE Signal Processing Letters 1997,4(6):182-183. 10.1109/97.586043View ArticleGoogle Scholar
  10. Torkkola K: Blind separation of delayed and convolved sources. In Unsupervised Adaptive Filtering. Vol. I. John Wiley & Sons, New York, NY, USA; 2000. chapter 8Google Scholar
  11. Hansen LK, Dyrholm M: A prediction matrix approach to convolutive ICA. Proceedings of the 13th IEEE Workshop on Neural Networks for Signal Processing (NNSP '03), September 2003, Toulouse, France 249-258.Google Scholar
  12. Comon P: Independent component analysis. A new concept? Signal Processing 1994,36(3):287-314. 10.1016/0165-1684(94)90029-9View ArticleMATHGoogle Scholar
  13. Ding Z, (Geoffrey) Li Y: Blind Equalization and Identification. Marcel Dekker, New York, NY, USA; 2000.Google Scholar
  14. Bellanger MG: Adaptive Digital Filters. Marcel Dekker, New York, NY, USA; 1987.MATHGoogle Scholar
  15. Haykin S: Adaptive Filter Theory. Prentice-Hall, Englewood Cliffs, NJ, USA; 1996.MATHGoogle Scholar
  16. Cavalcante CC, Montalvão JR, Dorizzi B, Mota JCM: A neural predictor for blind equalization in digital communication. Adaptive Systems for Signal Processing, Communication and Control (AS-SPCC '00), October 2000, Lake Louise, Alberta, CanadaGoogle Scholar
  17. Ferrari R, Panazio CM, Attux RRF, et al.: Unsupervised channel equalization using fuzzy prediction-error filters. Proceedings of IEEE Workshop on Neural Networks for Signal Processing XIII (NNSP '03), September 2003, Toulouse, France 869-878.Google Scholar
  18. Mendel JM: Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE 1995,83(3):345-377. 10.1109/5.364485View ArticleGoogle Scholar
  19. Patra SK: Development of fuzzy system based channel equalisers, Ph.D. thesis. University of Edinburgh, Edinburgh, Scotland, UK; 1998.Google Scholar
  20. Merz P: An iterated local search approach for minimum sum-of-squares clustering. Proceedings of the 5th International Symposium on Intelligent Data Analysis (IDA '03), August 2003, Berlin, Germany 1680-1686.Google Scholar

Copyright

© Suyama et al. 2007

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