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

TL;DR

This paper introduces a physics-informed neural network that reconstructs near-surface sound fields from sparse data to accurately infer complex surface impedance in real-world conditions, enabling practical in-situ acoustic characterization.

Contribution

It presents a novel neural field approach combined with a compact microphone array for fast, accurate impedance inference from limited measurements in realistic environments.

Findings

Accurate impedance retrieval with few sensors demonstrated in lab and real-world tests.

Method operates within seconds to minutes, suitable for practical applications.

Robust characterization of boundary conditions in architectural and automotive contexts.

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.