Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Flexible Independent Component Analysis
Journal of VLSI Signal Processing Systems
Blind separation methods based on Pearson system and its extensions
Signal Processing
Super-Gaussian mixture source model for ICA
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
EURASIP Journal on Advances in Signal Processing - Special issue on microphone array speech processing
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In this contribution, we propose an entirely novel family of flexible score functions for blind source separation (BSS), based on the family of generalized gamma densities. To blindly extract the independent source signals, we resort to the popular FastICA approach, whilst to adaptively estimate the parameters of such score functions, we use an efficient method based on maximum likelihood (ML). Experimental results with sources employing a wide range of statistical distributions, indicate that the proposed flexible FastICA (FF-ICA) technique significantly outperforms conventional independent component analysis (ICA) methods, which operate only on a fixed score function regime.