A bounded component analysis approach for the separation of convolutive mixtures of dependent and independent sources

Abstract

Bounded Component Analysis is a new framework for Blind Source Separation problem. It allows separation of both dependent and independent sources under the assumption about the magnitude boundedness of sources. This article proposes a novel Bounded Component Analysis optimization setting for the separation of the convolutive mixtures of sources as an extension of a recent geometric framework introduced for the instantaneous mixing problem. It is shown that the global maximizers of this setting are perfect separators. The article also provides the iterative algorithm corresponding to this setting and the numerical examples to illustrate its performance especially for separating convolutive mixtures of sources that are correlated in both space and time dimensions.