Active control of impulsive noise with on-line outlier detection

Conventional active noise control algorithms may fail to achieve convergence in the presence of non-Gaussian or impulsive noise. Typical workarounds for this problem are based on the concept of perturbing as little as possible the filter weight update process by discarding or discounting samples associated with outliers. The effectiveness of these methods depends on how accurately outliers can be detected while maintaining a sufficiently low algorithm complexity. A simple on-line recursive procedure is here suggested that reliably estimates amplitude thresholds for outlier detection. The resulting adaptive scheme for noise control can be shown to outperform existing methods, both for invariant and time-varying noise distributions.