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dc.contributor.authorNAKAMURA, Een_US
dc.contributor.authorYoshii, Ken_US
dc.contributor.authorDixon, Sen_US
dc.date.accessioned2017-08-16T10:42:54Z
dc.date.available2017-06-20en_US
dc.date.issued2017-06-30en_US
dc.date.submitted2017-08-11T14:05:21.507Z
dc.identifier.issn2329-9290en_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/25265
dc.description.abstractThis paper presents a statistical method for use in music transcription that can estimate score times of note onsets and offsets from polyphonic MIDI performance signals. Because performed note durations can deviate largely from score-indicated values, previous methods had the problem of not being able to accurately estimate offset score times (or note values) and thus could only output incomplete musical scores. Based on observations that the pitch context and onset score times are influential on the configuration of note values, we construct a context-tree model that provides prior distributions of note values using these features and combine it with a performance model in the framework of Markov random fields. Evaluation results show that our method reduces the average error rate by around 40 percent compared to existing/simple methods. We also confirmed that, in our model, the score model plays a more important role than the performance model, and it automatically captures the voice structure by unsupervised learning.en_US
dc.format.extent1 - 1en_US
dc.relation.ispartofIEEE/ACM Transactions on Audio, Speech, and Language Processingen_US
dc.titleNote Value Recognition for Piano Transcription Using Markov Random Fieldsen_US
dc.typeArticle
dc.rights.holder© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.identifier.doi10.1109/TASLP.2017.2722103en_US
pubs.notesNo embargoen_US
pubs.publication-statusPublisheden_US
dcterms.dateAccepted2017-06-20en_US


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