Abstract
This paper introduces new contrast functions for blind separation of sources with different time-frequency signatures. Two contrast functions based on the Kullback-Leibler and Jensen-Rényi divergences in the time-frequency (T-F) plane are introduced. Two iterative algorithms are proposed for the proposed contrasts optimization and source separation. One algorithm consists of spatial whitening and gradient-Jacobi maximization, combining Givens rotations and stochastic gradient. The second algorithm uses a quasi-Newton technique.
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Sahmoudi, M., Amin, M.G., Abed-Meraim, K., Belouchrani, A. (2006). Contrast Functions for Blind Source Separation Based on Time-Frequency Information-Theory. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_109
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DOI: https://doi.org/10.1007/11679363_109
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