Boundary-Informed Sound Field Reconstruction

2025-06-16

AI Summary

This study addresses high-precision spatial acoustic field reconstruction under sparse or uncertain boundary priors. We propose a Bayesian acoustic field inversion method that jointly incorporates geometric and physical constraints. Our key contributions are: (i) the first explicit integration of impedance boundary conditions into a linear Bayesian framework, yielding a boundary-aware prior; and (ii) joint estimation of noise variance, signal variance, and boundary impedance hyperparameters, enabling data-driven, adaptive modeling. The method achieves significant improvements in high-frequency and large-area acoustic field reconstruction using only数百 boundary point-cloud measurements—despite decimeter-level positional uncertainty—outperforming boundary-agnostic baselines. Experiments demonstrate robustness and physical interpretability under limited microphone measurements. By effectively leveraging incomplete and noisy boundary information, our approach establishes a new paradigm for acoustic field reconstruction in realistic scenarios where boundary conditions are partially known or imprecisely characterized.

Technology Category

• Machine Learning: Bayesian Learning
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

We consider the problem of reconstructing the sound field in a room using prior information of the boundary geometry, represented as a point cloud. In general, when no boundary information is available, an accurate sound field reconstruction over a large spatial region and at high frequencies requires numerous microphone measurements. On the other hand, if all geometrical and acoustical aspects of the boundaries are known, the sound field could, in theory, be simulated without any measurements. In this work, we address the intermediate case, where only partial or uncertain boundary information is available. This setting is similar to one studied in virtual reality applications, where the goal is to create a perceptually convincing audio experience. In this work, we focus on spatial sound control applications, which in contrast require an accurate sound field reconstruction. Therefore, we formulate the problem within a linear Bayesian framework, incorporating a boundary-informed prior derived from impedance boundary conditions. The formulation allows for joint optimization of the unknown hyperparameters, including the noise and signal variances and the impedance boundary conditions. Using numerical experiments, we show that incorporating the boundary-informed prior significantly enhances the reconstruction, notably even when only a few hundreds of boundary points are available or when the boundary positions are calibrated with an uncertainty up to 1 dm.

Problem

• Reconstruct sound field using partial boundary geometry data
• Improve accuracy with limited microphone measurements
• Optimize impedance boundary conditions via Bayesian framework

Innovation

• Uses boundary-informed prior for sound field
• Employs linear Bayesian framework for optimization
• Enhances reconstruction with partial boundary data