A constrained least squares algorithm for fast Blind Source Separation in a non-stationary mixing environment

Abstract

This paper proposes a Constrained Least Square approach to the problem of Blind Source Separation (BSS) in a non-stationary mixing environment. Initially the demixing matrix is identified for the nominal system using the standard Kullback-Liebler(KL) divergence minimization technique. The KL algorithm is computationally expensive requiring longer CPU time and a large collection of samples. Therefore for small or structured changes in the mixing system which may occur due to environmental conditions this algorithm may be slow and inappropriate in certain applications. In this paper we have proposed an algorithm based on Constrained Least Square that utilizes the initially estimated demixing structure from the KL algorithm to find the new structure for the changed system. It is computationally faster even for larger number of samples. The assumptions are that the changes are infrequent and the statistical properties of the sources do not change. The performance of the technique has been compared with existing methods.