arXiv:2611.09199 · v1
SAS: Scene-Annotated AudioSet Strong for Joint Scene-Event Analysis
摘要
Context is central to human auditory perception, as listeners interpret sounds within their acoustic environment to resolve ambiguity. However, large-scale datasets that jointly capture sound events and acoustic scenes remain limited. We address this by augmenting AudioSet Strong with clip-level scene annotations under two taxonomies for joint sceneevent modeling. A corpus-scale analysis shows that scene identity is better captured by events distinctive to a scene than by those that merely occur frequently. Reweighting event co-occurrences toward scene-specific associations produces representations that better align with human-derived taxonomies, while also exposing scenes where the acoustic and semantic views diverge. This geometric divergence predicts classification errors, while supervised validation confirms that the proposed scene annotations provide meaningful complementary information for developing scalable context-aware machine listening systems.
期刊信息：Proceedings of the 11th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2026), Oct 2026, Boston, MA, United States, United States