Source Separation in the Time-Frequency Domain by Maximizing an Information-Theoretic Criterion

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-Renyi 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 performance of the proposed method is illustrated and quantified through examples