SemiPL: A Semi-supervised Method for Event Sound Source Localization

arXiv:2404.19615v1 Announce Type: new
Abstract: In recent years, Event Sound Source Localization has been widely applied in various fields. Recent works typically relying on the contrastive learning framework show impressive performance. However, all work is based on large relatively simple datasets. It's also crucial to understand and analyze human behaviors (actions and interactions of people), voices, and sounds in chaotic events in many applications, e.g., crowd management, and emergency response services. In this paper, we apply the existing model to …

arxiv cs.cv cs.mm cs.sd eess.as event localization semi-supervised sound type