[논문]Quantitative analysis of piano performance proficiency focusing on difference between hands

Kim, Sarah; Park, Jeong Mi; Rhyu, Seungyeon; Nam, Juhan; Lee, Kyogu

doi:10.1371/journal.pone.0250299

[해외논문] Quantitative analysis of piano performance proficiency focusing on difference between hands 원문보기

PLoS ONE, v.16 no.5, 2021년, pp.e0250299 -

Kim, Sarah (Music and Audio Research Group, Department of Intelligence and Information, Seoul National University, Seoul, South Korea) , Park, Jeong Mi (Department of Transdisciplinary Studies, Seoul National University, Seoul, South Korea) , Rhyu, Seungyeon (Music and Audio Research Group, Department of Intelligence and Information, Seoul National University, Seoul, South Korea) , Nam, Juhan (Graduate School of Culture Technology, Korea Advanced Institute of Science and Technology, Daejeon, South Korea) , Lee, Kyogu (Music and Audio Research Group, Department of Intelligence and Information, Seoul National University, Seoul, South Korea)

Abstract ▼ AI-Helper

Quantitative evaluation of piano performance is of interests in many fields, including music education and computational performance rendering. Previous studies utilized features extracted from audio or musical instrument digital interface (MIDI) files but did not address the difference between hands (DBH), which might be an important aspect of high-quality performance. Therefore, we investigated DBH as an important factor determining performance proficiency. To this end, 34 experts and 34 amateurs were recruited to play two excerpts on a Yamaha Disklavier. Each performance was recorded in MIDI, and handcrafted features were extracted separately for the right hand (RH) and left hand (LH). These were conventional MIDI features representing temporal and dynamic attributes of each note and computed as absolute values (e. g., MIDI velocity) or ratios between performance and corresponding scores (e. g., ratio of duration or inter-onset interval (IOI)). These note-based features were rearranged into additional features representing DBH by simple subtraction between features of both hands. Statistical analyses showed that DBH was more significant in experts than in amateurs across features. Regarding temporal features, experts pressed keys longer and faster with the RH than did amateurs. Regarding dynamic features, RH exhibited both greater values and a smoother change along melodic intonations in experts that in amateurs. Further experiments using principal component analysis (PCA) and support vector machine (SVM) verified that hand-difference features can successfully differentiate experts from amateurs according to performance proficiency. Moreover, existing note-based raw feature values (Basic features) and DBH features were tested repeatedly via 10-fold cross-validation, suggesting that adding DBH features to Basic features improved F1 scores to 93.6% (by 3.5%) over Basic features. Our results suggest that differently controlling both hands simultaneously is an important skill for pianists; therefore, DBH features should be considered in the quantitative evaluation of piano performance.

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표제어: PCR

동의어: Packet Collision Rate

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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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