Robust active noise control: An information theoretic learning approach
Published in Applied Acoustics, 2016
Nonlinear active noise control (ANC) systems, which employ a nonlinear filter as the adaptive controlleris not robust when the primary noise to be mitigated has a non-Gaussian distribution. The algorithmwhich updates the weights of the controller may even diverge for some higher magnitude primary noisesignals. With an objective to improve the robustness of nonlinear ANC systems, a correntropy based non-linear ANC system is developed in this paper. The proposed ANC scheme uses an information theoreticlearning approach and has been shown to provide robust noise mitigation even for non-Gaussian primarynoise signals.
Citation
‘Kurian, Nikhil Cherian, Kashyap Patel, and Nithin V. George. “Robust active noise control: An information theoretic learning approach.” Applied Acoustics 117 (2017): 180-184.’