{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/large-scale-cover-song-detection-in-digital","title":"Large-Scale Cover Song Detection in Digital Music Libraries Using Metadata, Lyrics and Audio Features","arxiv_id":"1808.10351","date":"2018-08-30","proceeding":null,"authors":["Correya Albin Andrew","Hennequin Romain","Arcos Mickaël"],"abstract":"Cover song detection is a very relevant task in Music Information Retrieval\n(MIR) studies and has been mainly addressed using audio-based systems. Despite\nits potential impact in industrial contexts, low performances and lack of\nscalability have prevented such systems from being adopted in practice for\nlarge applications. In this work, we investigate whether textual music\ninformation (such as metadata and lyrics) can be used along with audio for\nlarge-scale cover identification problem in a wide digital music library. We\nbenchmark this problem using standard text and state of the art audio\nsimilarity measures. Our studies shows that these methods can significantly\nincrease the accuracy and scalability of cover detection systems on Million\nSong Dataset (MSD) and Second Hand Song (SHS) datasets. By only leveraging\nstandard tf-idf based text similarity measures on song titles and lyrics, we\nachieved 35.5% of absolute increase in mean average precision compared to the\ncurrent scalable audio content-based state of the art methods on MSD. These\nexperimental results suggests that new methodologies can be encouraged among\nresearchers to leverage and identify more sophisticated NLP-based techniques to\nimprove current cover song identification systems in digital music libraries\nwith metadata.","url_abs":"http://arxiv.org/abs/1808.10351v1","url_pdf":"http://arxiv.org/pdf/1808.10351v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"large-scale-cover-song-detection-in-digital","repo_url":"https://github.com/deezer/cover_song_detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"cover-song-identification","task_name":"Cover song identification"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"music-information-retrieval","task_name":"Music Information Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"text-similarity","task_name":"text similarity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}