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.

翻译：标准化实验室吸声材料表征依赖于理想化声场假设，这与实际工况存在显著偏差。因此，原位声学表征已成为精确诊断与虚拟原型构建的关键。本研究提出一种物理信息神经场方法，通过稀疏声压样本重建局部近表面宽带声场，以直接推断复表面阻抗。并行多频架构设计使得宽带阻抗反演可在数秒至数分钟量级的运行时间内完成。为验证该方法，我们研制了硬件复杂度较低的紧凑型麦克风阵列。数值验证与实验室实验表明，该方法能在实际工况下使用少量传感器实现精确阻抗反演。我们进一步在车辆座舱中展示了该方法的应用，为规避强干扰的测量点位选择提供实践指导。本研究证明，该方法为建筑声学与汽车声学领域的原位边界条件表征提供了鲁棒的技术手段。