Promoting convergence in multi-channel blind signal separation using PNLMS

The proportionate normalized least-mean squares (PNLMS) adaptation algorithm exploits the sparse nature of acoustic impulse responses and assigns adaptation gain proportional to the absolute value of filter coefficients, thereby resulting in faster convergence. In the past it has shown to improve convergence of acoustic paths in echo-cancellation applications. In this paper, we investigate the use of PNLMS algorithm for blind speech separation and show that with a careful selection of operating parameters the PNLMS algorithm greatly helps promote convergence of the un-mixing filters when compared to the conventional normalized least-mean-squares (NLMS) adaptation. The PNLMS based blind speech separation is suitable for real-time implementation as it promises faster convergence and requires only a modest increase in complexity as compared to the NLMS algorithm.