Selective-partial-update NLMS and affine projection algorithms for acoustic echo cancellation

Adaptive filters used in acoustic echo cancellation often require a large number of coefficients to model the acoustic echo path with sufficient accuracy. The computational complexity of adaptation algorithms such as the NLMS algorithm is proportional to the filter length, which means that for long filters the adaptation task can become prohibitively expensive. The purpose of partial coefficient update is to reduce the computational complexity by adapting a subset of the filter coefficients at every iteration. In this paper we develop selective-partial-update NLMS and AP algorithms based on the principle of minimum disturbance. The algorithms are based on sound theoretical justification and appear to have good convergence performance as attested to by computer simulations with real speech signals