{"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/outer-product-hidden-markov-model-and","title":"Outer-Product Hidden Markov Model and Polyphonic MIDI Score Following","arxiv_id":"1404.2313","date":"2014-04-08","proceeding":null,"authors":["Eita Nakamura","Tomohiko Nakamura","Yasuyuki Saito","Nobutaka Ono","Shigeki Sagayama"],"abstract":"We present a polyphonic MIDI score-following algorithm capable of following\nperformances with arbitrary repeats and skips, based on a probabilistic model\nof musical performances. It is attractive in practical applications of score\nfollowing to handle repeats and skips which may be made arbitrarily during\nperformances, but the algorithms previously described in the literature cannot\nbe applied to scores of practical length due to problems with large\ncomputational complexity. We propose a new type of hidden Markov model (HMM) as\na performance model which can describe arbitrary repeats and skips including\nperformer tendencies on distributed score positions before and after them, and\nderive an efficient score-following algorithm that reduces computational\ncomplexity without pruning. A theoretical discussion on how much such\ninformation on performer tendencies improves the score-following results is\ngiven. The proposed score-following algorithm also admits performance mistakes\nand is demonstrated to be effective in practical situations by carrying out\nevaluations with human performances. The proposed HMM is potentially valuable\nfor other topics in information processing and we also provide a detailed\ndescription of inference algorithms.","url_abs":"http://arxiv.org/abs/1404.2313v1","url_pdf":"http://arxiv.org/pdf/1404.2313v1.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":"outer-product-hidden-markov-model-and","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":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}