Input signal decorrelation applied to adaptive second-order Volterra filters in the time domain

The compensation of nonlinear signal distortions as caused by small, low-cost loudspeakers driven at high volume by second-order Volterra filters has been investigated in recent works. In the field of acoustic echo cancellation, such undesired signal components are removed by adaptive filtering. Since the performance of the usually applied LMS adaptation suffers from correlations of the input data, preceding decorrelation of the excitation signal is desirable in order to increase the speed of convergence. This contribution discusses an efficient configuration for incorporating a decorrelation filter into nonlinear adaptive filtering scenarios. Simulation results for both noise and speech indicate an increase in convergence speed by providing decorrelated signals as filter input.