Performance enhancement of adaptive Active Noise Control systems for FMRI machines

Active Noise Control (ANC) of fMRI acoustic noise using the conventional Filtered-X LMS (FXLMS) approach results in poor cancelation performance and slow convergence due to its broadband nature and the need for high order adaptive filters. High order adaptive filters are needed to effectively model the long acoustic impulse responses. Existing methods to improve the performance of FXLMS based broadband ANC systems are either computationally expensive or need elaborate implementation. In this paper we show a practical method to enhance the performance of FXLMS based algorithms, by deriving a crude estimate of the causalWiener filter and initializing the adaptive filter with the estimated Wiener filter. We observe that very fast convergence to the global minimum can be achieved along with huge gains in the noise cancelation performance. We call this method Wiener initialized FXLMS (WI-FXLMS).We show the effectiveness of the proposed approach for the active noise control of functional MRI acoustic noise and several other realistic noise sources.