{"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/real-time-audio-to-score-alignment-of-music","title":"Real-Time Audio-to-Score Alignment of Music Performances Containing Errors and Arbitrary Repeats and Skips","arxiv_id":"1512.07748","date":"2015-12-24","proceeding":null,"authors":["Tomohiko Nakamura","Eita Nakamura","Shigeki Sagayama"],"abstract":"This paper discusses real-time alignment of audio signals of music\nperformance to the corresponding score (a.k.a. score following) which can\nhandle tempo changes, errors and arbitrary repeats and/or skips (repeats/skips)\nin performances. This type of score following is particularly useful in\nautomatic accompaniment for practices and rehearsals, where errors and\nrepeats/skips are often made. Simple extensions of the algorithms previously\nproposed in the literature are not applicable in these situations for scores of\npractical length due to the problem of large computational complexity. To cope\nwith this problem, we present two hidden Markov models of monophonic\nperformance with errors and arbitrary repeats/skips, and derive efficient\nscore-following algorithms with an assumption that the prior probability\ndistributions of score positions before and after repeats/skips are independent\nfrom each other. We confirmed real-time operation of the algorithms with music\nscores of practical length (around 10000 notes) on a modern laptop and their\ntracking ability to the input performance within 0.7 s on average after\nrepeats/skips in clarinet performance data. Further improvements and extension\nfor polyphonic signals are also discussed.","url_abs":"http://arxiv.org/abs/1512.07748v1","url_pdf":"http://arxiv.org/pdf/1512.07748v1.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":"real-time-audio-to-score-alignment-of-music","repo_url":"https://github.com/dartmouth-cs98/20w-ensemble-vr-score-following","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1512.07748","atlas_url":"https://app.syntology.ai/?focus=1512.07748","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}