Active noise control using wavelet function and network approach

Abstract

Active noise control (ANC) is based on the principle of superposition of waves. It means that an algorithm is used to tune a secondary source to make an anti-noise with equal amplitude but opposite phase with the primary source. In this paper, a wavelet function and network (WAVENET) approach is designed for ANC. The algorithm is used to train parameters of an anti-noise filter for omitting the undesired noise. FXLMS and NLMS are the conventional methods of ANC that need complex acoustic plant models and these necessities make the methods complex and inaccurate. In the WAVENET approach, this complexity can be accounted for. Numerical simulations for a WAVENET approach are presented to demonstrate the performance of the WAVENET approach scheme.