A joint block diagonalization approach to convolutive blind source separation

This paper is concerned with blind separation of convolutive sources. The main idea is to make an explicit exploitation of block Toeplitz structure and block-inner diagonal structure in autocorrelation matrices of source signals at different time delays as well as of inherent relations among these matrices. With implementation of joint block diagonalization, a tri-quadratic cost function is introduced so that the mixture matrix can be extracted from a set of the correlation matrices of the observed vector sequence without pre-whitening. In this novel one-stage algorithm, every iteration step involves finding the closed solution to the corresponding least squares problem. Once the estimate of the mixing matrix is obtained, the source signals are retrieved by the classical least squares methods. The performance of the proposed algorithm is illustrated by simulation results.