Low-artifact Source Separation Using Probabilistic Latent Component Analysis

We propose a method based on the probabilistic latent componentanalysis (PLCA) in which we use exponential distributions as priorsto decrease the activity level of a given basis vector. A straightforwardapplication of this method is when we try to extract a desiredsource from a mixture with low artifacts. For this purpose, we proposea maximum a posteriori (MAP) approach to identify the commonbasis vectors between two sources. A low-artifact estimate cannow be obtained by using a constraint such that the common basisvectors in the interfering signal’s dictionary tend to remain inactive.We discuss applications of this method in source separationwith similar-gender speakers and in enhancing a speech signal thatis contaminated with babble noise. Our simulations show that theproposed method not only reduces the artifacts but also increasesthe overall quality of the estimated signal.QC 20130724