Design and implementation of a nonlinear acoustic echo canceller

Abstract

An acoustic echo canceller based on neural networks and a fast affine projection (FAP) algorithm is introduced. This structure allows a large set of trade-offs between convergence rate, residual error, tracking capacity, and arithmetic complexity. Hence, the proposed structure has the potential for solving other difficult nonlinear adaptive signal processing tasks such as system identification where nonlinearity and nonstationarity are both important factors. To investigate the feasibility of practical implementation of the proposed structure, a hardware/software implementation is discussed. The specific objective is to find an implementation that satisfies all the system design constraints at least cost.