Surface impedance inference via neural fields and sparse acoustic data obtained by a compact array
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917269786853376 |
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| author | Xia, Yuanxin Li, Xinyan Calafà, Matteo Engsig-Karup, Allan P. Jeong, Cheol-Ho |
| author_facet | Xia, Yuanxin Li, Xinyan Calafà, Matteo Engsig-Karup, Allan P. Jeong, Cheol-Ho |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11425 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Surface impedance inference via neural fields and sparse acoustic data obtained by a compact array Xia, Yuanxin Li, Xinyan Calafà, Matteo Engsig-Karup, Allan P. Jeong, Cheol-Ho Sound Machine Learning 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. |
| title | Surface impedance inference via neural fields and sparse acoustic data obtained by a compact array |
| topic | Sound Machine Learning |
| url | https://arxiv.org/abs/2602.11425 |