Direction-Aware Neural Acoustic Fields for Few-Shot Interpolation of Ambisonic Impulse Responses

2025/05/19 by Christopher Ick, Ick, Christopher, Gordon Wichern +7 · 3 citations
Computer Science · Engineering · #Acoustic Wave Phenomena Research #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Sound (cs.SD) #Speech and Audio Processing #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2505.13617

openalex publication_date 2025/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The characteristics of a sound field are intrinsically linked to the geometric and spatial properties of the environment surrounding a sound source and a listener. The physics of sound propagation is captured in a time-domain signal known as a room impulse response (RIR). Prior work using neural fields (NFs) has allowed learning spatially-continuous representations of RIRs from finite RIR measurements. However, previous NF-based methods have focused on monaural omnidirectional or at most binaural listeners, which does not precisely capture the directional characteristics of a real sound field at a single point. We propose a direction-aware neural field (DANF) that more explicitly incorporates the directional information by Ambisonic-format RIRs. While DANF inherently captures spatial relations between sources and listeners, we further propose a direction-aware loss. In addition, we investigate the ability of DANF to adapt to new rooms in various ways including low-rank adaptation.

Citations

• Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark
• Room impulse response reconstruction with physics-informed deep learning
• Room impulse response reconstruction with physics-informed deep learning
• Neural Acoustic Context Field: Rendering Realistic Room Impulse Response With Neural Fields
• AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis
• SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning
• Learning Neural Acoustic Fields
• LoRA: Low-Rank Adaptation of Large Language Models
• Matterport3D: Learning from RGB-D Data in Indoor Environments
• Image method for efficiently simulating small-room acoustics

Cited by

• Differentiable Grouped Feedback Delay Networks for Learning Coupled Volume Acoustics
• Low-Rank Adaptation of Deep Prior Neural Networks For Room Impulse Response Reconstruction
• Physics-Informed Direction-Aware Neural Acoustic Fields

Related

• Physics-Informed Direction-Aware Neural Acoustic Fields
• Neural Acoustic Context Field: Rendering Realistic Room Impulse Response With Neural Fields
• Learning Neural Acoustic Fields
• Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation
• Neural Sound Field Decomposition with Super-resolution of Sound Direction