A generalized maximum correntropy criterion based robust sparse adaptive room equalization

Abstract

An adaptive room equalization scheme is usually employed to compensate for the distortion of sound produced by the room impulse response, thereby offering an improved listening experience. In a conventional adaptive room equalizer, an adaptive filter updated using a filtered-x least mean square (FxLMS) algorithm is used to achieve room equalization. Conventional FxLMS algorithm based room equalizers are not robust to strong disturbances picked up by the reference microphone. A robust adaptive room equalization scheme based on a generalized maximum correntropy criteria has been developed in this paper. The performance has been further enhanced by using a proportionate learning strategy to take advantage of the sparse nature of the room impulse response. The proposed algorithm has been shown to provide enhanced room equalization performance over other methods compared, for various types of noise distributions.