A nonlinear prediction approach to the blind separation of convolutive mixtures
EURASIP Journal on Applied Signal Processing
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WSEAS Transactions on Signal Processing
Generalized identifiability conditions for blind convolutive MIMO separation
IEEE Transactions on Signal Processing
A new behavior of higher order blind source separation methods for convolutive mixture
Digital Signal Processing
A general algebraic algorithm for blind extraction of one source in a MIMO convolutive mixture
IEEE Transactions on Signal Processing
Blind signal separation through cooperating ANNs
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
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This paper addresses the problem of blind separation of convolutive mixtures via contrast maximization. New frequency domain contrast functions are constructed based on higher order spectra of the observations. They allow to separate mixtures of sources that are spatially independent and temporally possibly nonlinear processes. Using Parseval's formula, the former criteria yield a general class of time-domain contrasts, which extends to the convolutive case results that have been previously obtained in the context either of instantaneous mixtures or of independent and identically distributed (i.i.d.) sources. A Monte Carlo simulation study is carried out for comparison between the different contrasts, thus providing a guideline about the choice of an appropriate contrast.