Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Oblique projectors-based blind source separation using information maximization principle
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Convolutive blind speech separation by decorrelation
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Blind signal separation using oblique projection operators method
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
Convolutive blind source separation by fourth-order statistics
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
A multistage approach to blind separation of convolutive speech mixtures
Speech Communication
Acoustic parameter extraction from occupied rooms utilizing blind source separation
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Second-order blind signal separation with optimal step size
Speech Communication
Centrality and mode detection in dynamic contact graphs; a joint diagonalisation approach
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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A new approach for convolutive blind source separation (BSS) by explicitly exploiting the second-order nonstationarity of signals and operating in the frequency domain is proposed. The algorithm accommodates a penalty function within the cross-power spectrum-based cost function and thereby converts the separation problem into a joint diagonalization problem with unconstrained optimization. This leads to a new member of the family of joint diagonalization criteria and a modification of the search direction of the gradient-based descent algorithm. Using this approach, not only can the degenerate solution induced by a unmixing matrix and the effect of large errors within the elements of covariance matrices at low-frequency bins be automatically removed, but in addition, a unifying view to joint diagonalization with unitary or nonunitary constraint is provided. Numerical experiments are presented to verify the performance of the new method, which show that a suitable penalty function may lead the algorithm to a faster convergence and a better performance for the separation of convolved speech signals, in particular, in terms of shape preservation and amplitude ambiguity reduction, as compared with the conventional second-order based algorithms for convolutive mixtures that exploit signal nonstationarity.