Surface impedance inference via neural fields and sparse acoustic data obtained by a compact array

• URL: http://arxiv.org/abs/2602.11425v1
• Date: Wed, 11 Feb 2026 22:56:46 GMT
• Title: Surface impedance inference via neural fields and sparse acoustic data obtained by a compact array
• Authors: Yuanxin Xia, Xinyan Li, Matteo Calafà, Allan P. Engsig-Karup, Cheol-Ho Jeong,
• Abstract summary: We propose a physics-informed neural field that reconstructs local, near-surface broadband sound fields from sparse pressure samples.<n>A parallel, multi-frequency architecture enables a broadband impedance retrieval within runtimes on the order of seconds to minutes.<n>Here, we show that this approach offers a robust means of characterizing emphin-situ boundary conditions for architectural and automotive acoustics.
• Score: 2.0687656230706155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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