Adaptive blind separation with an unknown number of sources
Neural 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
Computational Intelligence and Security
A Two-Step Blind Extraction Algorithm of Underdetermined Speech Mixtures
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
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
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Underdetermined blind separation of non-sparse sources using spatial time-frequency distributions
Digital Signal Processing
A new blind method for separating M+1 sources from M mixtures
Computers & Mathematics with Applications
Noisy component extraction (NoiCE)
IEEE Transactions on Circuits and Systems Part I: Regular Papers
A novel approach for underdetermined blind sources separation in frequency domain
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
ICA and committee machine-based algorithm for cursor control in a BCI system
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
ICA based semi-supervised learning algorithm for BCI systems
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Analysis of source sparsity and recoverability for SCA based blind source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Estimation of delays and attenuations for underdetermined BSS in frequency domain
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Blind extraction of singularly mixed source signals
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
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This paper presents a general approach to sequential blind extraction of instantaneously mixed sources for several major ill-conditioned cases as well as the regular case of full column rank mixing matrices. Four ill-conditioned cases are considered: The mixing matrix is square but singular; the number of sensors is less than that of sources; the number of sensors is larger than that of sources, but the column rank of the mixing matrix is deficient; and the number of sources is unknown and the column rank of the mixing matrix is deficient. First, a solvability analysis is presented for a general case. A necessary and sufficient condition for extractability is derived. A sequential blind extraction approach is then proposed to extract all theoretically separable sources. Next, a principle and a cost function based on fourth-order cumulants are presented for blind source extraction. By minimizing the cost function under a nonsingularity constraint of the extraction matrix, all theoretically separable sources can be extracted sequentially. Finally, simulation results are presented to demonstrate the validity and performance of the blind source extraction approach