A nonlinear prediction approach to the blind separation of convolutive mixtures

Authors:
Ricardo Suyama;Leonardo Tomazeli Duarte;Rafael Ferrari;Leandro Elias Paiva Rangel;Romis Ribeiro de Faissol Attux;Charles Casimiro Cavalcante;Fernando José Von Zuben;João Marcos Travassos Romano
Affiliations:
Laboratory of Signal Processing for Communications (DSPCOM), School of Electrical and Computer Engineering, University of Campinas (Unicamp), Campinas, SP, Brazil;Laboratory of Signal Processing for Communications (DSPCOM), School of Electrical and Computer Engineering, University of Campinas (Unicamp), Campinas, SP, Brazil;Laboratory of Signal Processing for Communications (DSPCOM), School of Electrical and Computer Engineering, University of Campinas (Unicamp), Campinas, SP, Brazil;Laboratory of Signal Processing for Communications (DSPCOM), School of Electrical and Computer Engineering, University of Campinas (Unicamp), Campinas, SP, Brazil;Laboratory of Signal Processing for Communications (DSPCOM), School of Electrical and Computer Engineering, University of Campinas (Unicamp), Campinas, SP, Brazil;Wireless Telecommunications Research Group (GTEL), Federal University of Ceará (UFC), Fortaleza, CE, Brazil;Laboratory of Bioinformatics and Bio-inspired Computing (LBiC), School of Electrical and Computer Engineering, University of Campinas (Unicamp), Campinas, SP, Brazil;Laboratory of Signal Processing for Communications (DSPCOM), School of Electrical and Computer Engineering, University of Campinas (Unicamp), Campinas, SP, Brazil
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2007

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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 ecacy of the proposed method is illustrated by some examples.