Approaching the time dependent cocktail party problem with online sparse coding neural gas

Abstract

We show how the "Online Sparse Coding Neural Gas" algorithm can be applied to a more realistic model of the "Cocktail Party Problem". We consider a setting where more sources than observations are given and additive noise is present. Furthermore, we make the model even more realistic, by allowing the mixing matrix to change slowly over time. We also process the data in an online pattern-by-pattern way where each observation is presented only once to the learning algorithm. The sources are estimated immediately from the observations. In order to evaluate the influence of the change rate of the time dependent mixing matrix and the signal-to-noise ratio on the reconstruction performance with respect to the underlying sources and the true mixing matrix, we use artificial data with known ground truth. © 2009 Springer-Verlag Berlin Heidelberg.