Nonlinear acoustic echo cancellation with fast converging memoryless preprocessor

Low-cost audio components in hands-free telephone applications call for nonlinear adaptive echo cancellation (AEC). It has been demonstrated that a cascade of a polynomial and an FIR filter can cancel such nonlinear echoes (Stenger and Rabenstein 1998). Another technique employs a hard-clipping curve with LMS adapted saturation parameter (Nollet and Jones 1997). For both cascaded systems we derive an LMS-type adaptation using a common framework, and propose stepsize normalizations for both approaches. To achieve sufficiently fast convergence for practical use, an RLS-type adaptation for the polynomial is derived and experimentally verified. Both techniques are compared using real hardware and speech signals, and show robust convergence behaviour and an echo reduction gain of up to 10 dB compared to a linear AEC