Physics-Informed Direction-Aware Neural Acoustic Fields

2025-07-09

AI Summary

This work addresses the insufficient spatial fidelity in first-order Ambisonics (FOA) room impulse response (RIR) modeling. We propose a physics-informed neural network (PINN) framework that jointly models the four-channel FOA sound field in the time–frequency domain by incorporating both the wave equation and the Helmholtz equation. Crucially, we introduce two novel physics-based priors derived from the fundamental relationships between particle velocity and FOA channel representations: one enforces partial differential constraints linking the W channel to the X, Y, and Z channels, thereby ensuring acoustic realizability of the output. Experiments on RIR interpolation demonstrate that our method significantly outperforms unconstrained baseline models, yielding improved physical consistency and enhanced spatial localization accuracy in reconstructed signals. The approach establishes a new paradigm for high-fidelity, interpretable sound-field modeling tailored to immersive audio generation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Low Level & Physics-based Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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.

Problem

• Modeling first-order Ambisonic room impulse responses using PINN
• Incorporating physics-informed priors for FOA RIR spatial characteristics
• Improving immersive audio generation with physically feasible relationships

Innovation

• Physics-informed neural network models FOA RIRs
• Derives priors linking particle velocity and FOA channels
• Imposes physical constraints on four FOA channels