Modified NLMF adaptation of Volterra filters used for nonlinear acoustic echo cancellation
When dealing with adaptive nonlinear filters used in acoustic echo cancellation, the tradeoff between convergence rate and steady-state error is an important issue. This tradeoff is favorably addressed here using a modified version of the normalized least-mean-fourth (NLMF) algorithm applied to an adaptive second-order Volterra structure. A convergence rate improvement is obtained at the same steady-state error by amending the step size of the conventional NLMF algorithm using a new step-size function that depends on the norm of an error vector of a certain length. The efficiency of the proposed method is compared with that of the conventional adapted second-order Volterra filter for nonlinear acoustic echo cancellation in terms of echo return loss enhancement (ERLE). From simulations conducted for input signals with different probability density functions, the new approach is shown to outperform the normalized least-mean-square (NLMS) second-order Volterra filter in terms of the convergence rate of the same steady-state error. The proposed technique was also tested in acoustic scenarios with impulse noise and separately with distinct local signal powers. The performance of the method was also compared to that of the traditional NLMF algorithm and of the variable NLMF (XE-NLMF) algorithm.
volterra filters convergence rate adaptive algorithms error norm