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Title: Statistical Piano Reduction Controlling Performance Difficulty
Authors: Nakamura, Eita  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0003-4097-6027 (unconfirmed)
Yoshii, Kazuyoshi  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0001-8387-8609 (unconfirmed)
Author's alias: 中村, 栄太
吉井, 和佳
Keywords: Symbolic Music Processing
Automatic Music Arrangement
Statistical Modelling
Issue Date: 2018
Publisher: Cambridge University Press (CUP)
Journal title: APSIPA Transactions on Signal and Information Processing
Volume: 7
Thesis number: e13
Abstract: We 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.
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.
URI: http://hdl.handle.net/2433/245465
DOI(Published Version): 10.1017/ATSIP.2018.18
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