Robust parallel virtual sensing method for feedback active noise control in a headrest

Abstract

Additional filter method (AFM) and remote microphone method (RMM) are the two common virtual sensing methods used to compensate for active noise control (ANC) in headrest systems where mounting the error microphone at the desired zone is inconvenient. AFM and RMM are divided into two stages: the training and control stages. In the training stage, a temporary microphone is installed at the target to generate the additional and observation filters for AFM and RMM, respectively. However, they are fully effective only when the disturbances are consistent during both the training and control stages. To solve this dilemma, we propose a robust parallel virtual sensing method (PVSM) for multichannel adaptive feedback ANC system to attenuate the varying narrowband noises. During the training stage, the potential primary noises are used to model a group of parallel additional filters. Subsequently, the synthetic observation filter required for estimating the virtual error signal is derived using minimax optimisation. In the control stage, the most eligible additional filter that minimises the energy of the estimated virtual error signal is matched per frame based on the minimum-mean-square-estimated-error-matching mechanism and applied to the adaptive controller. Theoretical analysis validates that when the primary source varies, PVSM is more robust than both AFM and RMM, and performs noise reduction similar to the direct control with the temporary error microphone placed at the target. The proposed PVSM is further analysed in terms of its convergence condition and computational complexity. Numerous simulations and real-time experiments conducted using a dual-channel ANC headrest validate the feasibility of the proposed algorithm and present a guideline for the selection of frame length in PVSM. Therefore, the proposed PVSM can be considered a robust virtual sensing method against varying primary disturbances.