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

Leveraging Geometrical Acoustic Simulations of Spatial Room Impulse Responses for Improved Sound Event Detection and Localization