Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A robust method to count and locate audio sources in a stereophonic linear instantaneous mixture
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
IEEE Transactions on Audio, Speech, and Language Processing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Blind extraction of intermittent sources
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
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In the case of a determined linear instantaneous mixture, a method to estimate non-stationnary sources with non activity periods is proposed. The method is based on the assumption that speech signals are inactive in some unknown temporal periods. Such silence periods allow to estimate the rows of the demixing matrix by a new algorithm called Direction Estimation of Separating Matrix (DESM). The periods of sources inactivity are estimated by a generalised eigen decomposition of covariance matrices of the mixtures, and the separating matrix is then estimated by a kernel principal component analysis. Experiments are provided with determined mixtures, and shown to be efficient.