# Learning Acoustic Scattering Fields for Dynamic Interactive Sound Propagation

arXiv: https://arxiv.org/abs/2010.04865 · HTML: https://mlsanity.com/paper/2010.04865/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Improving aircraft performance using machine learning: a review](https://mlsanity.com/paper/2210.11481/) (2022)
- [Listen2Scene: Interactive material-aware binaural sound propagation for reconstructed 3D scenes](https://mlsanity.com/paper/2302.02809/) (2023)
- [A Deep Learning Framework for Three Dimensional Shape Reconstruction from Phaseless Acoustic Scattering Far-field Data](https://mlsanity.com/paper/2407.09525/) (2024)
- [DynFOA: Generating First-Order Ambisonics with Conditional Diffusion for Dynamic and Acoustically Complex 360-Degree Videos](https://mlsanity.com/paper/2602.06846/) (2026)
- [DynFOA: Generating First-Order Ambisonics with Conditional Diffusion for Dynamic and Acoustically Complex 360-Degree Videos](https://mlsanity.com/paper/2604.02781/) (2026)

## Abstract (arXiv)

We present a novel hybrid sound propagation algorithm for interactive applications. Our approach is designed for dynamic scenes and uses a neural network-based learned scattered field representation along with ray tracing to generate specular, diffuse, diffraction, and occlusion effects efficiently. We use geometric deep learning to approximate the acoustic scattering field using spherical harmonics. We use a large 3D dataset for training, and compare its accuracy with the ground truth generated using an accurate wave-based solver. The additional overhead of computing the learned scattered field at runtime is small and we demonstrate its interactive performance by generating plausible sound effects in dynamic scenes with diffraction and occlusion effects. We demonstrate the perceptual benefits of our approach based on an audio-visual user study.