Low-artifact source separation using probabilistic latent component analysis

We propose a method based on the probabilistic latent component analysis (PLCA) in which we use exponential distributions as priors to decrease the activity level of a given basis vector. A straightforward application of this method is when we try to extract a desired source from a mixture with low artifacts. For this purpose, we propose a maximum a posteriori (MAP) approach to identify the common basis vectors between two sources. A low-artifact estimate can now be obtained by using a constraint such that the common basis vectors in the interfering signal´s dictionary tend to remain inactive. We discuss applications of this method in source separation with similar-gender speakers and in enhancing a speech signal that is contaminated with babble noise. Our simulations show that the proposed method not only reduces the artifacts but also increases the overall quality of the estimated signal.