Abstract
This paper studies an enhanced robust kernel least mean square (KLMS) adaptive filtering algorithm for nonlinear acoustic echo cancellation (NLAEC) in impulsive noise environment. Robust KLMS algorithm based on M-estimate theory shows robustness to simulated, Contaminated Gaussian (CG) impulsive noise. However, it fails to combat real-world impulsive noise which normally consists of a few consecutive impulsive samples. In this work, the linear prediction (LP) scheme is applied to the KLMS algorithm to detect and cancel the impulsive noise. The resultant LP-based KLMS (LPKLMS) algorithm thus can achieve improved robustness to the real-world impulsive noise which is frequently encountered in NLAEC and other applications alike.
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