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定量分析钢琴演奏技巧,重点关注双手差异。

Quantitative analysis of piano performance proficiency focusing on difference between hands.

机构信息

Music and Audio Research Group, Department of Intelligence and Information, Seoul National University, Seoul, South Korea.

Department of Transdisciplinary Studies, Seoul National University, Seoul, South Korea.

出版信息

PLoS One. 2021 May 19;16(5):e0250299. doi: 10.1371/journal.pone.0250299. eCollection 2021.

Abstract

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.

摘要

钢琴演奏的量化评估在音乐教育和计算演奏渲染等多个领域都具有重要意义。先前的研究利用从音频或乐器数字接口 (MIDI) 文件中提取的特征,但没有解决双手(DBH)之间的差异,而这可能是高质量演奏的一个重要方面。因此,我们研究了 DBH 作为决定演奏水平的重要因素。为此,我们招募了 34 名专家和 34 名业余爱好者在雅马哈 Disklavier 上演奏两个片段。每次演奏都以 MIDI 录制,并分别为右手 (RH) 和左手 (LH) 提取手工制作的特征。这些特征是代表每个音符的时间和动态属性的常规 MIDI 特征,并作为绝对值(例如 MIDI 速度)或演奏与相应乐谱之间的比率(例如持续时间或音程间间隔 (IOI) 的比率)计算。这些基于音符的特征通过双手特征之间的简单减法重新排列为代表 DBH 的附加特征。统计分析表明,DBH 在专家中的重要性高于业余爱好者中的重要性,涉及到各种特征。就时间特征而言,专家用右手比业余爱好者按下琴键的时间更长,速度更快。就动态特征而言,RH 表现出更大的值,并且在专家中比在业余爱好者中沿着旋律语调的变化更平滑。进一步使用主成分分析(PCA)和支持向量机(SVM)的实验验证了手差特征可以根据演奏水平成功地区分专家和业余爱好者。此外,通过 10 倍交叉验证反复测试了基于音符的原始特征值(基本特征)和 DBH 特征,表明将 DBH 特征添加到基本特征中可以将 F1 分数提高到 93.6%(提高 3.5%)。我们的结果表明,同时控制双手是钢琴家的一项重要技能;因此,在钢琴演奏的量化评估中应考虑 DBH 特征。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc18/8133499/02484b446202/pone.0250299.g001.jpg

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