Acoustic environments affected by impulsive disturbances are often governed by heavy-tailed \(\alpha \) -stable behaviour, under which conventional active noise control (ANC) algorithms based on squared-error adaptation may exhibit unstable convergence and undesirable transient responses. Although a variety of robust ANC techniques have been proposed to improve impulsive-noise tolerance, most still rely on continuous cancellation-oriented adaptation in the presence of large outliers. This paper introduces Active Noise Blunting (ANB), an event-driven framework that moderates impulsive acoustic disturbances through bounded waveform reshaping rather than strict residual cancellation. During detected impulsive intervals, the proposed method applies nonlinear soft clipping together with finite impulse response (FIR) smoothing to generate a perceptually moderated target waveform. Anti-noise is then constructed using a deterministic difference-to-target mechanism, yielding a controlled residual response instead of aggressive cancellation-driven transients. An operator-level analysis is used to establish bounded residual behaviour of the proposed framework under α-stable excitation without relying on recursive coefficient adaptation, step-size tuning, or second-order statistical assumptions. Simulation results obtained under different impulsive-noise conditions show consistent moderation of impulsive peaks, attenuation of high-frequency transient content in the 2–3.5 kHz region, improvement in output SNR, and noticeable reduction in crest factor relative to conventional FxLMS-type ANC methods. The results suggest that perceptual waveform reshaping may provide a practical alternative for impulsive-noise mitigation in acoustic environments where maintaining stable adaptive cancellation becomes difficult under heavy-tailed excitation.
