Date of this Version
1-1-2007
Document Type
Article
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.
Recommended Citation
Abu-Taleb, A. S.; Zayed, E. M.E.; El-Sayed, W. M.; Badawy, A. M.; and Mohammed, O. A., "Multichannel blind deconvolution using a generalized Gaussian source model" (2007). Electrical and Computer Engineering Faculty Publications. 114.
https://digitalcommons.fiu.edu/ece_fac/114
