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

arXiv: https://arxiv.org/abs/2602.11425 · HTML: https://mlsanity.com/paper/2602.11425/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 46 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [SOAP: Improving and Stabilizing Shampoo using Adam](https://mlsanity.com/paper/2409.11321/) (2024)
- [A data-driven two-microphone method for in-situ sound absorption measurements](https://mlsanity.com/paper/2502.04143/) (2025)
- [In situ estimation of the acoustic surface impedance using simulation-based inference](https://mlsanity.com/paper/2509.08873/) (2025)

## Built on by (most cited first, 1 of 1)

- [Physics-informed neural operators for the in situ characterization of locally reacting sound absorbers](https://mlsanity.com/paper/2604.07412/) (2026)

## Abstract (arXiv)

Standardized laboratory characterizations for absorbing materials rely on idealized sound field assumptions, which deviate largely from real-life conditions. Consequently, 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 in-situ boundary conditions for architectural and automotive acoustics.