Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Blind source separation based on time-frequency signalrepresentations
IEEE Transactions on Signal Processing
Measuring time-frequency information content using the Renyi entropies
IEEE Transactions on Information Theory
Contrast functions for blind source separation based on time-frequency information-theory
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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Blind source separation aims at recovering the original source signals given only observations of their mixtures. Some common approaches to the source separation problem include second or higher order statistics based methods, and independent component analysis. Most of these methods are developed in the time domain, and thus inherently assume the stationarity of the underlying signals. Since most real life signals of interest are non–stationary, there have been efforts to perform source separation in the time–frequency domain. In this paper, we propose a new approach for source separation on the time–frequency plane using an information–theoretic cost function. Jensen–Rényi divergence, as adapted to time–frequency distributions, is introduced as an effective cost function to extract sources that are disjoint on the time–frequency plane. The sources are extracted through a series of Givens rotations and the optimal rotation angle is found using the steepest descent algorithm. The proposed method is applied to several example signals to illustrate its effectiveness and the performance is quantified through simulations.