{"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/chord-label-personalization-through-deep","title":"Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations","arxiv_id":"1706.09552","date":"2017-06-29","proceeding":null,"authors":["H. V. Koops","W. B. de Haas","J. Bransen","A. Volk"],"abstract":"The increasing accuracy of automatic chord estimation systems, the\navailability of vast amounts of heterogeneous reference annotations, and\ninsights from annotator subjectivity research make chord label personalization\nincreasingly important. Nevertheless, automatic chord estimation systems are\nhistorically exclusively trained and evaluated on a single reference\nannotation. We introduce a first approach to automatic chord label\npersonalization by modeling subjectivity through deep learning of a harmonic\ninterval-based chord label representation. After integrating these\nrepresentations from multiple annotators, we can accurately personalize chord\nlabels for individual annotators from a single model and the annotators' chord\nlabel vocabulary. Furthermore, we show that chord personalization using\nmultiple reference annotations outperforms using a single reference annotation.","url_abs":"http://arxiv.org/abs/1706.09552v1","url_pdf":"http://arxiv.org/pdf/1706.09552v1.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":"chord-label-personalization-through-deep","repo_url":"https://github.com/hvkoops/chordlabelpersonalization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.09552","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}