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In situ estimation of the acoustic surface impedance using simulation-based inference

JM Schmid, JD Schmid, M Eser, S Marburg - arXiv preprint arXiv …, 2025 - arxiv.org
Computer Science paper cs.SD Suggest

… The approach employs simulation-based inference, which leverages the expressiveness of modern neural network architectures to directly map simulated data to posterior …

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BibTeX

@article{2509.08873v1,
Author        = {Jonas M. Schmid and Johannes D. Schmid and Martin Eser and Steffen Marburg},
Title         = {In situ estimation of the acoustic surface impedance using
  simulation-based inference},
Eprint        = {2509.08873v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.SD},
Abstract      = {Accurate acoustic simulations of enclosed spaces require precise boundary
conditions, typically expressed through surface impedances for wave-based
methods. Conventional measurement techniques often rely on simplifying
assumptions about the sound field and mounting conditions, limiting their
validity for real-world scenarios. To overcome these limitations, this study
introduces a Bayesian framework for the in situ estimation of
frequency-dependent acoustic surface impedances from sparse interior sound
pressure measurements. The approach employs simulation-based inference, which
leverages the expressiveness of modern neural network architectures to directly
map simulated data to posterior distributions of model parameters, bypassing
conventional sampling-based Bayesian approaches and offering advantages for
high-dimensional inference problems. Impedance behavior is modeled using a
damped oscillator model extended with a fractional calculus term. The framework
is verified on a finite element model of a cuboid room and further tested with
impedance tube measurements used as reference, achieving robust and accurate
estimation of all six individual impedances. Application to a numerical car
cabin model further demonstrates reliable uncertainty quantification and high
predictive accuracy even for complex-shaped geometries. Posterior predictive
checks and coverage diagnostics confirm well-calibrated inference, highlighting
the method's potential for generalizable, efficient, and physically consistent
characterization of acoustic boundary conditions in real-world interior
environments.},
Year          = {2025},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2509.08873v1},
File          = {2509.08873v1.pdf}
}

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