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

TL;DR

This paper introduces a physics-informed neural network-based active noise control system that interpolates soundfields from outside microphones, enabling effective noise reduction within a region of interest without placing microphones inside it.

Contribution

The paper presents a novel soundfield interpolation method using PINNs that improves noise control performance and user convenience compared to traditional ANC systems.

Findings

PINN outperforms spherical harmonic method in simulations

PINN-assisted ANC achieves greater noise reduction

Monitoring microphones can be placed outside ROI

Abstract

Conventional multiple-point active noise control (ANC) systems require placing error microphones within the region of interest (ROI), inconveniencing users. This paper designs a feasible monitoring microphone arrangement placed outside the ROI, providing a user with more freedom of movement. The soundfield within the ROI is interpolated from the microphone signals using a physics-informed neural network (PINN). PINN exploits the acoustic wave equation to assist soundfield interpolation under a limited number of monitoring microphones, and demonstrates better interpolation performance than the spherical harmonic method in simulations. An ANC system is designed to take advantage of the interpolated signal to reduce noise signal within the ROI. The PINN-assisted ANC system reduces noise more than that of the multiple-point ANC system in simulations.