A low-complexity parallel local remote microphone technology for multichannel narrowband active noise control systems

Narrowband active noise control (NANC) systems can effectively eliminate periodic disturbances at its error sensors, but sometimes the error sensors are inconvenient to be placed at target locations for a long period. Remote microphone technology (RMT) can tackle this problem by moving quiet zones to the target areas. Nevertheless, the RMT-NANC system has significantly higher computational complexity than conventional NANC systems, particularly for multichannel systems. This hinders their implementation in real-time systems with limited computational resources. The additional computational burden stems from multiple convolutions involving the estimated global high-order secondary paths and observation filters when estimating the virtual error signals. To address this problem, a low-complexity parallel local RMT is proposed based on the narrowband filtered-x least mean square (PLRMT-NFxLMS) algorithm in this paper. Using a two-step local modeling approach, multiple local modeling low-order filters for both secondary paths and observation filters are constructed during the training stage. In the subsequent control stage, virtual error signals are estimated in parallel for different frequency components using these filters instead of global modeling filters, thereby alleviating the substantial computational cost arising from convolutions between lengthy vectors. Moreover, a filtered-error structure, termed the PLRMTNFeLMS algorithm, is introduced in the proposed algorithm to further reduce the computational complexity. A comprehensive analysis of computational complexity is provided to demonstrate the superiority of the two proposed algorithms. Extensive simulations and real-time experiments were conducted to validate the feasibility and practicability of these proposed methods.