  This paper tackles the problem of novel view audio-visual synthesis along an
arbitrary trajectory in an indoor scene, given the audio-video recordings from
other known trajectories of the scene. Existing methods often overlook the
effect of room geometry, particularly wall occlusion to sound propagation,
making them less accurate in multi-room environments. In this work, we propose
a new approach called Scene Occlusion-aware Acoustic Field (SOAF) for accurate
sound generation. Our approach derives a prior for sound energy field using
distance-aware parametric sound-propagation modelling and then transforms it
based on scene transmittance learned from the input video. We extract features
from the local acoustic field centred around the receiver using a Fibonacci
Sphere to generate binaural audio for novel views with a direction-aware
attention mechanism. Extensive experiments on the real dataset RWAVS and the
synthetic dataset SoundSpaces demonstrate that our method outperforms previous
state-of-the-art techniques in audio generation. Project page:
https://github.com/huiyu-gao/SOAF/.
