Blind source separation for convolutive mixtures
Signal Processing
Genetic algorithms + data structures = evolution programs (3rd ed.)
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A fast fixed-point algorithm for independent component analysis
Neural Computation
Information-theoretic approach to blind separation of sources in non-linear mixture
Signal Processing - Special issue on neural networks
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Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
Multichannel signal separation: methods and analysis
IEEE Transactions on Signal Processing
Nonlinear blind source separation using a radial basis function network
IEEE Transactions on Neural Networks
Independent Component Analysis Aided Diagnosis of Cuban Spino Cerebellar Ataxia 2
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
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This paper presents a new adaptive procedure for the linear and non-linear separation of signals with non-uniform, symmetrical probability distributions, based on both simulated annealing (SA) and competitive learning (CL) methods by means of a neural network, considering the properties of the vectorial spaces of sources and mixtures, and using a multiple linearization in the mixture space. Also, the paper proposes the fusion of two important paradigms, Genetic Algorithms and the Blind Separation of Sources in Nonlinear Mixtures (GABSS). From experimental results, this paper demonstrates the possible benefits offered by GAs in combination with BSS, such as robustness against local minima, the parallel search for various solutions, and a high degree of flexibility in the evaluation function. The main characteristics of the method are its simplicity and the rapid convergence experimentally validated by the separation of many kinds of signals, such as speech or biomedical data.