用少量麦克风实现车内声学界面阻抗的快速精准反演
Surface impedance inference via neural fields and sparse acoustic data obtained by a compact array
- 基于物理约束的神经场模型，从稀疏声压数据重建近表面声场
- 仅需数个传感器，在秒级至分钟级内完成宽带阻抗反演
- 适用于车辆舱内等复杂场景，指导最优测量位置选择
标准实验室材料声学表征依赖理想声场假设，与实际环境偏差较大。因此，现场声学表征对精准诊断和虚拟原型设计至关重要。本文提出一种物理信息神经场方法，通过稀疏压力采样重建局部近表面宽带声场，直接推断复阻抗。采用并行多频架构，可在秒至分钟级别完成宽带阻抗反演。为验证方法，我们开发了一种硬件复杂的紧凑型麦克风阵列。数值验证与实验室实验均表明，在真实条件下仅用少量传感器即可实现高精度阻抗反演。进一步在车辆舱内应用该方法，有效指导测量点布局以规避强干扰。结果表明，该方法为建筑与汽车声学中的现场边界条件表征提供了稳健解决方案。
原文摘要 · Abstract (English)
Standardized laboratory characterizations for absorbing materials rely on idealized sound field assumptions, which deviate largely from real-life conditions. Consequently, \emph{in-situ} acoustic characterization has become essential for accurate diagnosis and virtual prototyping. We propose a physics-informed neural field that reconstructs local, near-surface broadband sound fields from sparse pressure samples to directly infer complex surface impedance. A parallel, multi-frequency architecture enables a broadband impedance retrieval within runtimes on the order of seconds to minutes. To validate the method, we developed a compact microphone array with low hardware complexity. Numerical verifications and laboratory experiments demonstrate accurate impedance retrieval with a small number of sensors under realistic conditions. We further showcase the approach in a vehicle cabin to provide practical guidance on measurement locations that avoid strong interference. Here, we show that this approach offers a robust means of characterizing \emph{in-situ} boundary conditions for architectural and automotive acoustics.
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