Physics-Informed Direction-Aware Neural Acoustic Fields

TL;DR

This paper introduces a physics-informed neural network model specifically designed for first-order Ambisonic room impulse responses, enhancing sound field interpolation by incorporating physical sound propagation principles.

Contribution

The paper extends PINNs to model FOA RIRs by deriving physics-informed priors based on particle velocity and FOA channels, improving modeling accuracy.

Findings

The physics-informed model outperforms non-physics-based neural networks.

Derived priors effectively encode physical relationships among FOA channels.

Experimental results validate the proposed method's effectiveness.

Abstract

This paper presents a physics-informed neural network (PINN) for modeling first-order Ambisonic (FOA) room impulse responses (RIRs). PINNs have demonstrated promising performance in sound field interpolation by combining the powerful modeling capability of neural networks and the physical principles of sound propagation. In room acoustics, PINNs have typically been trained to represent the sound pressure measured by omnidirectional microphones where the wave equation or its frequency-domain counterpart, i.e., the Helmholtz equation, is leveraged. Meanwhile, FOA RIRs additionally provide spatial characteristics and are useful for immersive audio generation with a wide range of applications. In this paper, we extend the PINN framework to model FOA RIRs. We derive two physics-informed priors for FOA RIRs based on the correspondence between the particle velocity and the (X, Y, Z)-channels of FOA. These priors associate the predicted W-channel and other channels through their partial derivatives and impose the physically feasible relationship on the four channels. Our experiments confirm the effectiveness of the proposed method compared with a neural network without the physics-informed prior.