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Physics-regularized neural acoustic fields for spatial layout inference from sparse room impulse responsesa).

Haoran Wang1, Shengzhi Qiang1, Qi Wang1

  • 1School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210023, China.

The Journal of the Acoustical Society of America
|July 15, 2026
PubMed
Summary

Estimating indoor layouts from sparse acoustic data is challenging. This study introduces a two-stage neural network approach to reconstruct acoustic fields and infer spatial layouts from limited room impulse responses (RIRs).

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Area of Science:

  • Acoustics
  • Computer Vision
  • Machine Learning

Background:

  • Sparse room impulse responses (RIRs) capture indoor acoustics but spatial layout estimation is ill-posed due to undersampling.
  • Existing methods struggle with limited RIR measurements, hindering applications in acoustic sensing and robotics.

Purpose of the Study:

  • To develop a robust framework for spatial layout inference using sparse RIR measurements.
  • To overcome the ill-posed nature of acoustic field reconstruction from limited data.

Main Methods:

  • A two-stage framework employing an implicit neural acoustic field for continuous acoustic representation.
  • Utilizing acoustic reciprocity as an unsupervised regularizer for improved interpolation.
  • A cross-modal acoustic-to-visual network for translating acoustic fields to spatial layout proxies.

Main Results:

  • Successfully reconstructed continuous acoustic fields from sparse RIRs.
  • Demonstrated reliable spatial layout inference using the proposed method.
  • Achieved improved performance on high-fidelity simulations and a public acoustic benchmark.

Conclusions:

  • The proposed two-stage framework effectively reconstructs acoustic fields and infers spatial layouts from sparse RIRs.
  • This method offers a promising solution for acoustic sensing and environmental perception in data-scarce scenarios.