@inproceedings{hachmeier-jaschke-2024-leveraging,
    title = "Leveraging User-Generated Metadata of Online Videos for Cover Song Identification",
    author = {Hachmeier, Simon  and
      J{\"a}schke, Robert},
    editor = "Kruspe, Anna  and
      Oramas, Sergio  and
      Epure, Elena V.  and
      Sordo, Mohamed  and
      Weck, Benno  and
      Doh, SeungHeon  and
      Won, Minz  and
      Manco, Ilaria  and
      Meseguer-Brocal, Gabriel",
    booktitle = "Proceedings of the 3rd Workshop on NLP for Music and Audio (NLP4MusA)",
    month = nov,
    year = "2024",
    address = "Oakland, USA",
    publisher = "Association for Computational Lingustics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.nlp4musa-1.8/",
    pages = "43--48",
    abstract = "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."
}