Blind Deconvolution of Sources in Fourier Space Based on Generalized Laplace Distribution

An approach to multi-channel blind deconvolution is developed, which uses an adaptive filter that performs blind source separation in the Fourier space. The approach keeps (during the learning process) the same permutation and provides appropriate scaling of components for all frequency bins in the frequency space. Experiments indicate that Generalized Laplace Distribution can be used effectively to blind deconvolution of convolution mixtures of sources in Fourier space compared Published in :International Journal of Computer Science & Communication Networks,Vol 1(3), 360-366 Blind Signal Separation Using an Adaptive Generalized Continuous Distribution Mohamed E. Waheed, Mohamed-H MOUSA, Mohamed-K HUSSEIN Abstract In this paper, we present an algorithm for the problem of independent component analysis (ICA) which can separate mixtures of suband superGaussian probability density distributions using a generalized continuous distribution source model. We use neural network representation to model the mixer and demixer respectively, and show how the parameters of the 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 exponential source model. The nonlinear function in ICA algorithem is self-adaptive and is controlled by the shape parameters of generalized exponential density model. Computer simulation results confirm the validity and high performance of the proposed algorithm.In this paper, we present an algorithm for the problem of independent component analysis (ICA) which can separate mixtures of suband superGaussian probability density distributions using a generalized continuous distribution source model. We use neural network representation to model the mixer and demixer respectively, and show how the parameters of the 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 exponential source model. The nonlinear function in ICA algorithem is self-adaptive and is controlled by the shape parameters of generalized exponential density model. Computer simulation results confirm the validity and high performance of the proposed algorithm.