Velocity Potential Neural Field for Efficient Ambisonics Impulse Response Modeling

2026-03-23

AI Summary

This work addresses the challenge that first-order Ambisonics (FOA) signals often fail to strictly satisfy acoustic physical constraints during spatial interpolation. To overcome this limitation, the authors propose a neural field-based approach that models the scalar velocity potential using a neural network. By leveraging automatic differentiation to accurately compute spatiotemporal partial derivatives of the learned potential function, the method directly generates four-channel FOA signals that rigorously adhere to the linearized momentum equation. This formulation recasts FOA reconstruction as a physics-informed neural field learning problem, ensuring that the output inherently complies with acoustic laws at any spatiotemporal point and circumventing the approximation errors associated with conventional soft-constraint strategies. Experimental results on room impulse response reconstruction demonstrate that the proposed method significantly enhances both spatial consistency and physical fidelity of the synthesized FOA signals.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

First-order Ambisonics (FOA) is a standard spatial audio format based on spherical harmonic decomposition. Its zeroth- and first-order components capture the sound pressure and particle velocity, respectively. Recently, physics-informed neural networks have been applied to the spatial interpolation of FOA signals, regularizing the network outputs based on soft penalty terms derived from physical principles, e.g., the linearized momentum equation. In this paper, we reformulate the task so that the predicted FOA signal automatically satisfies the linearized momentum equation. Our network approximates a scalar function called velocity potential, rather than the FOA signal itself. Then, the FOA signal can be readily recovered through the partial derivatives of the velocity potential with respect to the network inputs (i.e., time and microphone position) according to physics of sound propagation. By deriving the four channels of FOA from the single-channel velocity potential, the reconstructed signal follows the physical principle at any time and position by construction. Experimental results on room impulse response reconstruction confirm the effectiveness of the proposed framework.

Problem

• Ambisonics
• velocity potential
• physics-informed neural networks
• spatial audio
• impulse response modeling

Innovation

• velocity potential
• physics-informed neural networks
• first-order Ambisonics
• spatial audio
• neural fields