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This paper considers the underdetermined blind source separation (BSS) of convolutively mixed super-Gaussian signals that include speech, audio, and various other sparse signals. Here, the separation is performed in three steps. In the first and second steps, the mixing matrix and the sources at each time–frequency location are estimated by minimizing the Bayes risk (or the posterior risk) with squared...
This paper introduces a multi-class classification algorithm based on sparse representation which considers on rejection option to minimize risks caused by outliers. Here the outliers include signals that do not belong to any classes learned in a training step. To successfully reject the outliers, new rejection measure and corresponding dictionary learning algorithm are presented. Experimental results...
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