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Blind Separation of Sources Using Density Estimation and Simulated Annealing
Carlos G. PUNTONET Ali MANSOUR
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2001/10/01
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Digital Signal Processing
independent component analysis (ICA), decorrelation, high order statistics, density estimation, simulated annealing and geometrical approaches,
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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.