Subspace adaptive algorithm for blind separation of convolutive mixtures by conjugate gradient method
Résumé
In this paper, a new subspace adaptive algorithm, for blind separation of convolutive mixture, is proposed. This algorithm can be decomposed into two steps: At first, the convolutive mixture will be reduced to an instantaneous mixture (memoryless mixture), using a second-order statistics criterion based on subspace approach. The second step consists on the separation of the residual instantaneous mixture. The minimization of the criterion is achieved using a conjugate gradient method. The experimental results show that the convergence of our algorithm is improved thanks to the use of the conjugate gradient method. Finally, experimental results are shown.