Blind separation of sparse sources using Jeffrey's inverse prior and the em algorithm

Abstract

In this paper we study the properties of the Jeffrey's inverse prior for blind separation of sparse sources. This very sparse prior was previously used for Wavelet-based image denoising. In this paper we consider separation of 3 × 3 and 2 × 3 noisy mixtures of audio signals, decomposed on a MDCT basis. The hierarchical formulation of the inverse prior allows for EM-based computation of MAP estimates. This procedure happens to be fast when compared to a standard more complex Markov chain Monte Carlo method using the flexible Student t prior, with competitive results obtained. © Springer-Verlag Berlin Heidelberg 2006.