Separation of a class of convolutive mixtures: a contrast function approach

In this paper, we address the problem of the separation of convolutive mixtures in the case where the non-Gaussian source signals are not necessarily filtered versions of i.i.d. sequences. In this context, we show that the contrast functions, used in the linear process source case, still allow to separate the sources by a deflation approach. Some particular properties of higher order cumulants based contrast functions are also given.

[1]  J. Pesquet,et al.  Generalized contrasts for multichannel blind deconvolution of linear systems , 1997, IEEE Signal Processing Letters.

[2]  Christian Jutten,et al.  Blind source separation for convolutive mixtures , 1995, Signal Process..

[3]  Jitendra K. Tugnait,et al.  Identification and deconvolution of multichannel linear non-Gaussian processes using higher order statistics and inverse filter criteria , 1997, IEEE Trans. Signal Process..

[4]  Ananthram Swami,et al.  Multichannel ARMA processes , 1994, IEEE Trans. Signal Process..

[5]  Ehud Weinstein,et al.  Criteria for multichannel signal separation , 1994, IEEE Trans. Signal Process..

[6]  Nathalie Delfosse,et al.  Adaptive blind separation of independent sources: A deflation approach , 1995, Signal Process..

[7]  D. Donoho ON MINIMUM ENTROPY DECONVOLUTION , 1981 .

[8]  P. Comon Independent Component Analysis , 1992 .

[9]  Jitendra K Tugnait,et al.  On blind separation of convolutive mixtures of independent linear signals , 1996, Proceedings of 8th Workshop on Statistical Signal and Array Processing.

[10]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[11]  P. Loubaton,et al.  Blind deconvolution of multivariate signals: A deflation approach , 1993, Proceedings of ICC '93 - IEEE International Conference on Communications.

[12]  P. Comon,et al.  Contrasts for multichannel blind deconvolution , 1996, IEEE Signal Processing Letters.