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dc.contributor.authorNakamura, Eita
dc.contributor.authorYoshii, Kazuyoshi
dc.contributor.alternative中村, 栄太
dc.contributor.alternative吉井, 和佳
dc.date.accessioned2020-01-28T01:02:38Z-
dc.date.available2020-01-28T01:02:38Z-
dc.date.issued2018
dc.identifier.issn2048-7703
dc.identifier.urihttp://hdl.handle.net/2433/245465-
dc.description.abstractWe present a statistical-modeling method for piano reduction, i.e. converting an ensemble score into piano scores, that can control performance difficulty. While previous studies have focused on describing the condition for playable piano scores, it depends on player's skill and can change continuously with the tempo. We thus computationally quantify performance difficulty as well as musical fidelity to the original score, and formulate the problem as optimization of musical fidelity under constraints on difficulty values. First, performance difficulty measures are developed by means of probabilistic generative models for piano scores and the relation to the rate of performance errors is studied. Second, to describe musical fidelity, we construct a probabilistic model integrating a prior piano-score model and a model representing how ensemble scores are likely to be edited. An iterative optimization algorithm for piano reduction is developed based on statistical inference of the model. We confirm the effect of the iterative procedure; we find that subjective difficulty and musical fidelity monotonically increase with controlled difficulty values; and we show that incorporating sequential dependence of pitches and fingering motion in the piano-score model improves the quality of reduction scores in high-difficulty cases.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherCambridge University Press (CUP)
dc.rights© The Authors, 2018 This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.subjectSymbolic Music Processing
dc.subjectAutomatic Music Arrangement
dc.subjectStatistical Modelling
dc.titleStatistical Piano Reduction Controlling Performance Difficulty
dc.type.niitypeJournal Article
dc.identifier.jtitleAPSIPA Transactions on Signal and Information Processing
dc.identifier.volume7
dc.relation.doi10.1017/ATSIP.2018.18
dc.textversionpublisher
dc.identifier.artnume13
dc.addressGraduate School of Informatics, Kyoto University
dc.addressGraduate School of Informatics, Kyoto University・RIKEN Center for Advanced Intelligence Project
dc.identifier.kaken26700020 / 16H01744 / 16J05486 / 16H02917 / 16K00501
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