Active impulsive noise control using α-score-matched robust filtered-x normalized adaptation

Active noise control systems often degrade in environments with impulsive disturbances that follow heavy-tailed statistics. To address this challenge, we introduce Alpha Score Matched Active Impulsive Noise Control (α-SMANC), a normalized filtered-x strategy that employs a surrogate score function to represent the behaviour of alpha-stable processes. Stability is reinforced through a scale tracker designed with an exponentially weighted median and median absolute deviation, eliminating the need for variance-based measures. A potential function is developed to establish theoretical bounds on step size and ensure convergence. In near-Gaussian scenarios the method behaves like normalized LMS, while under impulsive noise it maintains robustness even in the presence of secondary-path mismatch. With linear computational cost in filter length, the approach is suitable for real-time execution. Simulation studies demonstrate reduced residual error and effective suppression of large impulsive bursts.