2024/12/16 by Simon Hachmeier, Hachmeier, Simon, Robert Jäschke +1 · 1 citation
Arts and Humanities · Computer Science · Engineering · #Computer science #Cover (algebra) #Diverse Musicological Studies #Engineering #Identification (biology) #Information retrieval #Metadata #Multimedia #Music History and Culture #Music and Audio Processing #World Wide Web
paper · pdf · doi:10.48550/arxiv.2412.11818
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/12/16 · openalex created_date 2024/12/18 · openalex updated_date 2026/07/28
YouTube is a rich source of cover songs. Since the platform itself is organized in terms of videos rather than songs, the retrieval of covers is not trivial. The field of cover song identification addresses this problem and provides approaches that usually rely on audio content. However, including the user-generated video metadata available on YouTube promises improved identification results. In this paper, we propose a multi-modal approach for cover song identification on online video platforms. We combine the entity resolution models with audio-based approaches using a ranking model. Our findings implicate that leveraging user-generated metadata can stabilize cover song identification performance on YouTube.