Blind Signal Separation Using an Adaptive Generalized Compound Gamma Distribution

Abstract

We propose an independent component analysis (ICA) algorithm which can separate mixtures of sub- and super- Gaussian source signals with self-adaptive nonlinearities. The ICA algorithm in the framework of natural Riemannian gradient, is derived using the parameterized Generalized Compound Gamma Distribution density model. The nonlinear function in ICA algorithem is self-adaptive and is controlled by the shape parameter of Adaptive Generalized Compound Gamma Distribution density model. Computer simulation results confirm the validity and high performance of the proposed algorithm