APSIPA Transactions on Signal and Information Processing > Vol 7 > Issue 1

Statistical piano reduction controlling performance difficulty

Eita Nakamura, Kyoto University, Japan, enakamura@sap.ist.i.kyoto-u.ac.jp , Kazuyoshi Yoshii, Kyoto University, Japan
 
Suggested Citation
Eita Nakamura and Kazuyoshi Yoshii (2018), "Statistical piano reduction controlling performance difficulty", APSIPA Transactions on Signal and Information Processing: Vol. 7: No. 1, e13. http://dx.doi.org/10.1017/ATSIP.2018.18

Publication Date: 13 Oct 2018
© 2018 Eita Nakamura and Kazuyoshi Yoshii
 
Subjects
 
Keywords
Symbolic Music ProcessingAutomatic Music ArrangementStatistical Modelling
 

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In this article:
I. INTRODUCTION 
II. QUANTITATIVE MEASURES OF PERFORMANCE DIFFICULTY 
III. PIANO REDUCTION METHOD 
IV. EVALUATION OF PIANO REDUCTION ALGORITHMS 
V. CONCLUSION 

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.

DOI:10.1017/ATSIP.2018.18