[3Win5] Poster session 3

[3Win5-100] Estimation of 3D sound field using physics-informed neural network with dynamic pulling method

〇Ken Kurata¹, Gen Sato¹, Izumi tsunokuni¹, Yusuke Ikeda¹ (1.Tokyo Denki University)

Keywords:PINNs, SIREN, sound field reconstruction, room acoustics

Room impulse response (RIR) is a fundamental measurement for obtaining information about sound propagation between a loudspeaker and a microphone. When measuring RIRs over a large area or with high spatial density, it becomes challenging to measure RIRs at numerous points. Recently, physics-informed neural networks (PINNs) have been applied to the problem of estimating the early RIRs from a small number of microphones. Generally, PINNs use two types of loss functions: one corresponding to physical laws and the other to data errors. A challenge with PINNs is that when the gradients associated with these two loss functions conflict, learning does not progress properly. In this study, we aimed to improve the estimation accuracies of 3D RIRs by applying PINNs with the Dynamic Pulling Method. From the simulation experiments in a 3D sound field, the proposed method achieved higher accuracy in estimating RIRs under noisy conditions compared to the conventional method.