2023/09/06 by Christopher Ick, Ick, Christopher, Brian McFee +1
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Music Technology and Sound Studies #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2309.03337
openalex publication_date 2023/09/06 · openalex created_date 2023/09/09 · openalex updated_date 2026/07/28
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∘ 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.