Blind source separation using order statistics

Authors:
Jani Even;Eric Moisan
Affiliations:
Laboratoire des Images et des Signaux, Groupe Non-Linéaire, ENSIEG, Domaine universitaire, Saint Martin d'Hères Cedex, France;Laboratoire des Images et des Signaux, Groupe Non-Linéaire, ENSIEG, Domaine universitaire, Saint Martin d'Hères Cedex, France
Venue:
Signal Processing
Year:
2005

Citing 8
Cited 7

Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
An information-maximization approach to blind separation and blind deconvolution

Neural Computation
Natural gradient works efficiently in learning

Neural Computation
Blind source separation via the second characteristic function

Signal Processing
A blind source separation technique using second-order statistics

IEEE Transactions on Signal Processing
Blind separation of instantaneous mixture of sources based on orderstatistics

IEEE Transactions on Signal Processing
Equivariant adaptive source separation

IEEE Transactions on Signal Processing
Nonlinear blind source separation using higher order statistics anda genetic algorithm

IEEE Transactions on Evolutionary Computation

Blind sparse source separation using cluster particle swarm optimization technique

AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
A robust blind sparse source separation algorithm using genetic algorithm to identify mixing matrix

SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
Blind separation with unknown number of sources based on auto-trimmed neural network

Neurocomputing
Blind source separation with dynamic source number using adaptive neural algorithm

Expert Systems with Applications: An International Journal
A robust blind sparse source separation algorithm using genetic algorithm to identify mixing matrix

SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
Mixing matrix identification for underdetermined blind signal separation: using hough transform and fuzzy K-means clustering

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Blind source separation of post-nonlinear mixtures using evolutionary computation and order statistics

ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation

Quantified Score

Hi-index	0.08

Visualization

Abstract

This paper shows the possibility to blindly separate instantaneous mixtures of sources by means of a criterion exploiting order statistics. Properties of higher order statistics and second-order methods are first underlined. Then a brief description of the order statistics shows that they gather all these properties and a new criterion is proposed. Next an iterative algorithm able to simultaneously extract all the sources is developed. The last part is comparison of this algorithm with well-known methods (JADE and SOBI). The most striking result is the possibility to exploit together independence and correlation through the use of order statistics.