Blind separation of any source distributions via high-order statistics
Signal Processing
Blind Deconvolution of MIMO-IIR Systems: A Two-Stage EVA
Neural Information Processing
Reference Based Contrast Functions in a Semi-blind Context
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Extraction of sources of tremor in hand movements of patients with movement disorders
IEEE Transactions on Information Technology in Biomedicine
MIMO-AR system identification and blind source separation for GMM-distributed sources
IEEE Transactions on Signal Processing
Generalized identifiability conditions for blind convolutive MIMO separation
IEEE Transactions on Signal Processing
Unbiased adaptive estimations of the fourth-order cumulant for real random zero-mean signal
IEEE Transactions on Signal Processing
A new behavior of higher order blind source separation methods for convolutive mixture
Digital Signal Processing
An eigenvector algorithm with reference signals using a deflation approach for blind deconvolution
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Convolutive blind speech separation by decorrelation
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Convolutive blind source separation by fourth-order statistics
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Source extraction by maximizing the variance in the conditional distribution tails
IEEE Transactions on Signal Processing
A general algebraic algorithm for blind extraction of one source in a MIMO convolutive mixture
IEEE Transactions on Signal Processing
The estimation of the fourth-order cumulant for dependent data: consistency and asymptotic normality
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
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This paper deals with the problem of source separation in the case when the observations result from a multiple-input multiple-output convolutive mixing system. In a blind framework, higher order contrast functions have been proved to be efficient for extracting sources. Inspired by a semiblind approach, we propose new contrast functions for blind signal separation that make use of reference signals. The main advantage of this approach consists in the quadratic form of these criteria: the extraction of one source hence reduces to a simple optimization task for which fast and efficient algorithms are available. The separation of the other sources from the mixture is then carried out by an iterative deflation method. Furthermore, these contrasts are shown to be valid for both independent identically distributed (i.i.d.) and non-i.i.d. source signals. The performance offered by these criteria is investigated through simulations: they appear as very appealing tools compared with some classical contrast functions