A narrowband active noise control system with a frequency estimator based on Bayesian inference

Narrowband active noise control (NANC) achieves good performance in controlling low-frequency periodic noise. Such control is necessary to obtain the frequency information of the primary periodic noise, including the number of frequency components and the individual frequency values, to generate reference signals. The performance of the NANC system mainly depends on the accuracy of frequency estimation of the primary periodic noise. In this article, a frequency estimator based on Bayesian inference that can obtain accurate frequencies as well as the number of frequency components is applied to a NANC system. Numerous computer simulations are conducted. Simulation results show that the proposed system outperforms a conventional NANC system with the adaptive notch filter (ANF) algorithm at low signal-to-noise-ratio (SNR) primary periodic noise.