IEICE TRANS. FUNDAMENTALS, VOL.E84-A, NO.10 OCTOBER 2001. 



Blind Separation of Sources using Density Estimationand Simulated Annealing 

C.G. Puntonet and A. Mansour

SUMMARY
This paper presents a new adaptive blind separation
of sources (BSS) method for linear and non-linear mixtures.
 The sources are assumed to be statistically independent
with non-uniform and symmetrical PDF.The algorithm is based 
on both simulated annealing and density estimation methods using
a neural network. Considering the properties of the vectorial 
spaces of sources and mixtures, and using some linearization
in the mixture space, the new method is derived. Finally, the main 
characteristics of the method are simplicity and the fast convergence
experimentally validated by the separation of many kinds 
of signals, such as speech or biomedical data. 

keywords:Independent Component Analysis(ICA),Decorrelation,
High Order Statistics, Density Estimation, Simulate 
Annealing and Geometrical Approaches.