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Conventional multiple-point active noise control (ANC) systems requireplacing error microphones within the region of interest (ROI), inconveniencingusers. This paper designs a feasible monitoring microphone arrangement placedoutside the ROI, providing a user with more freedom of movement. The soundfieldwithin the ROI is interpolated from the microphone signals using aphysics-informed neural network (PINN). PINN exploits the acoustic waveequation to assist soundfield interpolation under a limited number ofmonitoring microphones, and demonstrates better interpolation performance thanthe spherical harmonic method in simulations. An ANC system is designed to takeadvantage of the interpolated signal to reduce noise signal within the ROI. ThePINN-assisted ANC system reduces noise more than that of the multiple-point ANCsystem in simulations.

An Active Noise Control System Based on Soundfield Interpolation Using a Physics-informed Neural Network