A split kernel adaptive filtering architecture for nonlinear acoustic echo cancellation

We propose a new linear-in-the-parameters (LIP) nonlinear filter based on kernel methods to address the problem of nonlinear acoustic echo cancellation (NAEC). For this purpose we define a framework based on a parallel scheme in which any kernel-based adaptive filter (KAF) can be incorporated efficiently. This structure is composed of a classic adaptive filter on one branch, committed to estimating the linear part of the echo path, and a kernel adaptive filter on the other branch, to model the nonlinearities rebounding in the echo path. In addition, we propose a novel low-complexity least mean square (LMS) KAF with very few parameters, to be used in the parallel architecture. Finally, we demonstrate the effectiveness of the proposed scheme in real NAEC scenarios, for different choices of the KAF.