Multichannel blind deconvolution using a generalized Gaussian source model Article

Abu-Taleb, AS, Zayed, EME, El-Sayed, WM et al. (2007). Multichannel blind deconvolution using a generalized Gaussian source model . 12(1), 1-9. 10.3390/mca12010001

cited authors

  • Abu-Taleb, AS; Zayed, EME; El-Sayed, WM; Badawy, AM; Mohammed, OA

authors

abstract

  • In this paper, we present an algorithm for the problem of multi-channel blind deconvolution which can adapt to un-known sources with both sub-Gaussian and super-Gaussian probability density distributions using a generalized gaussian source model. We use a state space representation to model the mixer and demixer respectively, and show how the parameters of the demixer can be adapted using a gradient descent algorithm incorporating the natural gradient extension. We also present a learning method for the unknown parameters of the generalized Gaussian source model. The performance of the proposed generalized Gaussian source model on a typical example is compared with those of other algorithm, viz the switching nonlinearity algorithm proposed by Lee et al. [8]. © Association for Scientific Research.

publication date

  • January 1, 2007

Digital Object Identifier (DOI)

start page

  • 1

end page

  • 9

volume

  • 12

issue

  • 1