A Study on Difficulty Level Recognition of Piano Sheet Music

Chiu, Shih-Chuan; Chen, Min-Syan

doi:10.1109/ism.2012.11

Cited by 14 publications

(17 citation statements)

References 9 publications

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“…In previous studies [11,12], features such as playing speed, note density, pitch entropy, hand displacement rate, hand stretch, and fingering complexity have been considered to estimate the difficulty level of piano scores. These features are incorporated in the above difficulty measures, although in an implicit manner.…”

Section: Quantitative Measures Of Performance Difficultymentioning

confidence: 99%

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Statistical piano reduction controlling performance difficulty

Nakamura

Yoshii

2018

SIP

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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.

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Section: Quantitative Measures Of Performance Difficultymentioning

confidence: 99%

“…A possible solution is to quantify performance difficulty and use it as an indicator of playable scores in each situation of skill level, tempo, etc. [11,12].…”

Section: Introductionmentioning

confidence: 99%

Statistical piano reduction controlling performance difficulty

Nakamura

Yoshii

2018

SIP

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“…A representative example is presented in reference [28]. This work seeks to automatically generate or predict the difficulty of a piano music piece.…”

Section: Complexity Analysismentioning

confidence: 99%

“…Similar to [28], [43] studies the complexity of playing guitar. Their work extracts features that determine difficulty when playing guitar.…”

Section: Complexity Analysismentioning

confidence: 99%

“…LMA and Labanotation focus on the actual performance and the execution of a piece of music (or equivalently dance or many other physical motions), whereas Musiplectics focuses on the theoretical performance that has been specified by the piece of music. However, the categories studied in LMA have some rough analogues with both [28] and [43], and thus indirectly correlate with musical elements analyzed in Musiplectics.…”

Section: Complexity Analysismentioning

confidence: 99%

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Musiplectics: computational assessment of the complexity of music scores

Holder

Tilevich

Gillick

2015

2015 ACM International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!)

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In the Western classical tradition, musicians play music from notated sheet music, called a score. When playing music from a score, a musician translates its visual symbols into sequences of instrument-specific physical motions. Hence, a music score's overall complexity represents a sum of the cognitive and mechanical acuity required for its performance. For a given instrument, different notes, intervals, articulations, dynamics, key signatures, and tempo represent dissimilar levels of difficulty, which vary depending on the performer's proficiency. Individual musicians embrace this tenet, but may disagree about the degrees of difficulty. This thesis introduces musiplectics1 , a systematic and objective approach to computational assessment of the complexity of a music score for any instrument. Musiplectics defines computing paradigms for automatically and accurately calculating the complexity of playing a music score on a given instrument. The core concept codifies a two-phase process. First, music experts rank the relative difficulty of individual musical components (e.g., notes, intervals, dynamics, etc.) for different playing proficiencies and instruments. Second, a computing engine automatically applies this ranking to music scores and calculates their respective complexity. As a proof of concept of musiplectics, we present an automated, Web-based application called Musical Complexity Scoring (MCS) for music educators and performers. Musiplectics can engender the creation of practical computing tools for objective and expeditious assessment of a music score's suitability for the abilities of intended performers.This thesis is based on research submitted for publication at ONWARD'15.That this work received support from a Virginia Tech ICAT SEAD grant.1 musiplectics = music + plectics, Greek for the study of complexity DedicationTo the memory of Coppertone Holder, the standard by which all other pets shall be measured.iii Acknowledgments I have not experienced anything in this world quite like the publication of my master's thesis. It has taken only my last two years of graduate school to accomplish, but it has brought me (kicking and screaming at times) further along in those two years than I could have possibly imagined. This process took its toll many times not only on myself, but on all of those close to me. Thus, I'd like to take a moment to graciously acknowledge and thank all of those that had a hand in both shaping my research and keeping my sanity.

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