A mutually referenced blind multiuser separation of convolutive mixture algorithm

In this paper, we present a new subspace adaptive algorithm for the blind separation problem of a convolutive mixture. The major advantage of such an algorithm is that almost all the unknown parameters of the inverse channel can be estimated using only second-order statistics. In fact, a subspace approach was used to transform the convolutive mixture into an instantaneous mixture using a criterion of second-order statistics. It is known that the convergence of subspace algorithms is in general, very slow. To improve the convergence speed of our algorithm, a conjugate gradient method was used to minimize the subspace criterion. The experimental results show that the convergence of our algorithm is improved due to the use of the conjugate gradient method.