@MISC{Rowe99bayesianblind, author = {Daniel Rowe}, title = {Bayesian Blind Source Separation}, year = {1999} }

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Abstract

This paper presents a Bayesian statistical framework for blind source separation that unifies other approaches such as Principal Components, Independent Components, and Factor Analysis. Further, Ia probabilistic method is developed to determine the number of sources to separate and the advantages over other methods are stated. Keywords---Source separation, principal components, independent components, factor analysis, Bayesian separation. I. Introduction and Model T HE problem addressed by blind source separation is that of separating unobservable or latent source signals when mixed signals are observed. In other words, to take a set of observed mixed signal vectors and unmix or separate them into a set of true unobservable source signal vectors. This paper adopts a linear synthesis model where the observations are linear combinations of the sources and a Bayesian statistical approach. In the Bayesian approach to statistical inference, available prior information either from subject...