  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.
