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In the recent past, the Sparse Signal Recovery (SSR) problem has been very well studied using penalized regression approaches with different choice of penalty functions. In this work we revisit these penalized regression formulations in a Bayesian framework with suitable choice of supergaussian prior distributions. We introduce a generalized scale mixture framework, and provide connections with well...
In this article we study the sparse signal recovery problem in a Bayesian framework using a novel Bootstrapped Sparse Bayesian Learning method. Sparse Bayesian Learning (SBL) framework is an effective tool for pruning out the irrelevant features and ending up with a sparse representation. In SBL the choice of prior over the variances of the Gaussian Scale mixture has been an interesting area of research...
In this paper, it is shown that an appropriate model for voiced speech is an all-pole filter excited by a block sparse excitation sequence. The modeling approach is generalized in a novel manner to deal with a wide spectrum of speech signal; voiced speech, unvoiced speech and mixed excitation speech. In this context, the input sequence to the all-pole model is modeled as a suitable weighted linear...
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