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Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators.

Nikolas Borrel-Jensen1, Somdatta Goswami2, Allan P Engsig-Karup3

  • 1Department of Electrical and Photonics Engineering, Acoustic Technology, Technical University of Denmark, Kongens Lyngby 2800, Denmark.

Proceedings of the National Academy of Sciences of the United States of America
|January 4, 2024
PubMed
Summary

We developed a fast method using deep operator networks for 3D acoustic simulations. This enables real-time sound propagation prediction in virtual environments, overcoming computational limits of traditional methods.

Keywords:
DeepONetdomain decompositionoperator learningtransfer learningvirtual acoustics

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Area of Science:

  • Acoustics
  • Computational physics
  • Machine learning

Background:

  • Accurate acoustic simulations are crucial for virtual/augmented reality, game audio, and spatial computing.
  • Traditional numerical methods for solving the wave equation are computationally intensive, especially for parametric source positions.
  • This limits the practical application of detailed acoustic simulations in dynamic virtual environments.

Purpose of the Study:

  • To develop a computationally efficient method for predicting sound propagation in 3D virtual rooms with parametric source positions.
  • To overcome the limitations of conventional numerical discretization methods for wave phenomena.
  • To enable real-time acoustic simulations for applications like virtual reality and game audio.

Main Methods:

  • Utilizing deep operator networks to approximate linear wave-equation operators.
  • Developing a compact surrogate model to avoid pre-calculating and storing impulse responses.
  • Applying the method to various complex 3D acoustic scene geometries.

Main Results:

  • Achieved millisecond-scale computation times for sound propagation prediction.
  • Demonstrated good agreement with reference solutions across different scene complexities.
  • Reported root mean squared errors between 0.02 and 0.10 Pa.

Conclusions:

  • Deep operator networks offer a computationally efficient solution for 3D acoustic simulations with parametric source positions.
  • The proposed method represents a paradigm shift, enabling precise wave field predictions in realistic domains.
  • This advancement facilitates more immersive and realistic audio experiences in virtual and augmented reality applications.