  As deeper and more complex models are developed for the task of sound event
localization and detection (SELD), the demand for annotated spatial audio data
continues to increase. Annotating field recordings with 360$^{\circ}$ video
takes many hours from trained annotators, while recording events within
motion-tracked laboratories are bounded by cost and expertise. Because of this,
localization models rely on a relatively limited amount of spatial audio data
in the form of spatial room impulse response (SRIR) datasets, which limits the
progress of increasingly deep neural network based approaches. In this work, we
demonstrate that simulated geometrical acoustics can provide an appealing
solution to this problem. We use simulated geometrical acoustics to generate a
novel SRIR dataset that can train a SELD model to provide similar performance
to that of a real SRIR dataset. Furthermore, we demonstrate using simulated
data to augment existing datasets, improving on benchmarks set by state of the
art SELD models. We explore the potential and limitations of geometric acoustic
simulation for localization and event detection. We also propose further
studies to verify the limitations of this method, as well as further methods to
generate synthetic data for SELD tasks without the need to record more data.
