Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A quasi-stochastic gradient algorithm for variance-dependent component analysis
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Blind identification and source separation in 2×3 under-determined mixtures
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Complex random vectors and ICA models: identifiability, uniqueness, and separability
IEEE Transactions on Information Theory
Generalized identifiability conditions for blind convolutive MIMO separation
IEEE Transactions on Signal Processing
Controlled complete ARMA independent process analysis
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Contrast functions for independent subspace analysis
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
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This paper deals with the problem of Blind Source Separation. Contrary to the vast majority of works, we do not assume the statistical independence between the sources and explicitly consider that they are dependent. We introduce three particular models of dependent sources and show that their cumulants have interesting properties. Based on these properties, we investigate the behaviour of classical Blind Source Separation algorithms when applied to these sources: depending on the source vector, the separation may be sucessful or some additionnal indeterminacies can be identified.