Avoiding local trap in nonlinear acoustic echo cancellation with clipping compensation

For the nonlinear acoustic echo cancellation, we present an adaptive learning of the saturation effect of the amplifier and the room propagation in terms of the hard-clipping and the FIR system. The conventional learning algorithms are based on a gradient descent method, i.e., rely on local information, which results in a major drawback that the estimation of the hard-clipping is trapped in local minima. In this paper, we solve this drawback by exploiting global information embodied as a set including the desired hard-clipping with high-probability. The proposed adaptive learning of the hard-clipping is designed to track the sets with a projection-based algorithm. In the adaptive learning of the FIR system, we propose the use of the Huber loss function for the robustness against the error in the estimation of the hard-clipping. Numerical examples show that the proposed algorithm is never trapped in the local minima and has an excellent steady-state behavior.