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Related Experiment Video

Updated: Jun 12, 2025

A Method to Study Adaptation to Left-Right Reversed Audition

A Method to Study Adaptation to Left-Right Reversed Audition

Published on: October 29, 2018

Sound field reconstruction using a compact acoustics-informed neural network.

Fei Ma¹, Sipei Zhao¹, Ian S Burnett²

¹Center for Audio, Acoustics and Vibration, Faculty of Engineering and IT, University of Sydney Technology, Ultimo, New South Wales 2007, Australia.

The Journal of the Acoustical Society of America

|September 26, 2024

Summary

This study introduces a novel acoustics-informed neural network (AINN) for sound field reconstruction (SFR). The AINN method enhances accuracy and physical validity compared to traditional and data-driven approaches.

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Area of Science:

Acoustics
Signal Processing
Machine Learning

Background:

Conventional sound field reconstruction (SFR) methods, while efficient, may require excessive microphones.
Purely data-driven methods can be computationally intensive and may produce physically invalid results.

Purpose of the Study:

To propose a compact acoustics-informed neural network (AINN) for robust and physically valid sound field reconstruction.
To improve the accuracy and efficiency of SFR using a physics-regularized neural network.

Main Methods:

Developed a compact acoustics-informed neural network (AINN) that integrates the Helmholtz equation for regularization.
The AINN predicts sound pressures and gradients within a region of interest using boundary measurements.
Employed acoustic transfer functions measured in diverse environments for experimental validation.

Main Results:

The AINN method demonstrated superior performance compared to traditional cylindrical harmonics and singular value decomposition methods.
The integration of the Helmholtz equation enhanced the neural network's robustness against measurement variations.
AINN successfully generated physically valid sound field reconstructions.

Conclusions:

The proposed AINN offers a more robust, accurate, and computationally efficient approach to sound field reconstruction.
AINN provides a promising direction for advancing SFR by combining data-driven learning with physical acoustics principles.