Separating more sources than sensors using time-frequency distributions
EURASIP Journal on Applied Signal Processing
Blind source separation based on cumulants with time and frequency non-properties
IEEE Transactions on Audio, Speech, and Language Processing
Extraction of atrial activity from the ECG by spectrally constrained ICA based on kurtosis sign
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
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In this paper, we address the adaptive blind source separation of independent sources using higher order statistics. Although this problem was considered in numerous works, none of the existing algorithms is guaranteed to converge to a relevant solution. Here, we propose a new separation scheme whose convergence is proved analytically. It is based on the observation that it is possible to extract one of the source signals by a simple algorithm obtained by extending to the source separation context some of the ideas developed by Shalvi-Weinstein in the framework of blind deconvolution. A low cost deflation procedure allows the extraction of the other source signals by means of the same algorithm. Simulation results illustrate the behaviour of this separation method.