Double-talk robust fast converging algorithms for network echo cancellation

There is a need for echo cancelers for echo paths with long impulse responses (⩾64 ms). This in turn creates a need for more rapidly converging algorithms in order to meet the specifications for network echo cancelers. Faster convergence, however, in general implies a higher sensitivity to near-end disturbances, especially “double-talk.” Previously, a fast converging algorithm has been proposed called proportionate normalized least mean squares (PNLMS) algorithm. This algorithm exploits the sparseness of the echo path and has the advantage that no detection of active coefficients is needed. In this paper we propose a method for making the PNLMS algorithm more robust against double-talk. The slower divergence rate of these algorithms in combination with a standard Geigel double-talk detector improves the performance of a network echo canceler considerably during double-talk. The principle is based on a scaled nonlinearity which is applied to the residual error signal. This results in the robust PNLMS algorithm which diverges much slower than PNLMS and standard NLMS. Tradeoff between convergence and divergence rate is easily adjusted with one parameter and the added complexity is about seven instructions per sample which is less than 0.3% of the total load of a PNLMS algorithm with 512 filter coefficients. A generalization of the robust PNLMS algorithm to a robust proportionate affine projection algorithm (APA) is also presented. It converges very fast, and unlike PNLMS, is not as dependent on the assumption of a sparse echo path response. The complexity of the robust proportionate APA of order two is roughly the same as that of PNLMS