Blind Separation of Single Channel Mixture Using ICA Basis Functions

Abstract

A new technique has been developed to enable blind source separation given only a single channel recording. The proposed method infers source signals and their contribution factors at each time point by a number of adaptation steps maximizing log-likelihood of the estimated source parameters given the observed single channel data and sets of basis functions. This inferencing is possible due to the prior information on the inherent time structure of the sound sources by learning a priori sets of time-domain basis functions and the associated coefficient densities that encode the sources in a statistically efficient manner. A flexible model for density estimation allows accurate modeling of the observation and our experimental results show closeto-perfect separation on simulated mixtures as well as recordings in a real environment employing mixtures of two different sources.