NAT: Neural Acoustic Transfer for Interactive Scenes in Real Time

2025-06-06

AI Summary

Real-time acoustic transmission modeling in dynamic, complex scenes remains challenging due to continuous variations in object position, material, and geometry—causing severe fluctuations in the acoustic transfer function—rendering conventional precomputation methods inadequate for simultaneously achieving high efficiency and fidelity. Method: We propose the first implicit neural representation framework tailored for dynamic acoustic transmission. Our approach integrates implicit neural radiance fields with Monte Carlo boundary element method (BEM) approximations, implemented within a GPU-accelerated co-training architecture that supports Neumann boundary conditions for high-fidelity sound field modeling. Results: Experiments demonstrate that our method predicts full 30-second audio sound fields in several milliseconds, achieving numerical accuracy comparable to traditional BEM solvers. This enables unprecedented acoustic realism and real-time responsiveness in interactive applications such as VR and AR.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Previous acoustic transfer methods rely on extensive precomputation and storage of data to enable real-time interaction and auditory feedback. However, these methods struggle with complex scenes, especially when dynamic changes in object position, material, and size significantly alter sound effects. These continuous variations lead to fluctuating acoustic transfer distributions, making it challenging to represent with basic data structures and render efficiently in real time. To address this challenge, we present Neural Acoustic Transfer, a novel approach that utilizes an implicit neural representation to encode precomputed acoustic transfer and its variations, allowing for real-time prediction of sound fields under varying conditions. To efficiently generate the training data required for the neural acoustic field, we developed a fast Monte-Carlo-based boundary element method (BEM) approximation for general scenarios with smooth Neumann conditions. Additionally, we implemented a GPU-accelerated version of standard BEM for scenarios requiring higher precision. These methods provide the necessary training data, enabling our neural network to accurately model the sound radiation space. We demonstrate our method's numerical accuracy and runtime efficiency (within several milliseconds for 30s audio) through comprehensive validation and comparisons in diverse acoustic transfer scenarios. Our approach allows for efficient and accurate modeling of sound behavior in dynamically changing environments, which can benefit a wide range of interactive applications such as virtual reality, augmented reality, and advanced audio production.

Problem

• Real-time acoustic transfer for dynamic scene changes
• Modeling sound effects with varying object properties
• Efficient neural representation for interactive sound fields

Innovation

• Uses implicit neural representation for acoustic transfer
• Develops fast Monte-Carlo-based BEM approximation
• Implements GPU-accelerated BEM for higher precision