Performance Assessment Technologies for the Support of Musical Instrument Learning

Eremenko, Vsevolod; Morsi, Alia; Narang, Jyoti; Serra, Xavier

doi:10.5220/0009817006290640

Cited by 16 publications

(14 citation statements)

References 31 publications

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“…Most approaches to assessing musical performance use expertise to derive handcrafted features and then apply classification algorithms to predict expert ratings [ 9 , 11 ]. Music information retrieval (MIR) methods have been utilized in an attempt to estimate pitch accuracy [ 12 ], timing accuracy, rhythmic accuracy, tone quality, and the handcrafted features of complex attributes [ 13 – 16 ].…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of piano performance proficiency focusing on difference between hands

et al. 2021

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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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Section: Introductionmentioning

confidence: 99%

Quantitative analysis of piano performance proficiency focusing on difference between hands

et al. 2021

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“…The main contribution of this work is the interactive system for violin fingering generation, which can be applied to educational and pedagogical practices [2,10], as well as other performance-related works such as animation generation [7,9,12] and expressive music rendering [11]. We hope that this work can advance the research of intelligent systems for music performance [4,5,20].…”

Section: Introductionmentioning

confidence: 99%

Positioning Left-hand Movement in Violin Performance: A System and User Study of Fingering Pattern Generation

Jen

Chen

Sun

et al. 2021

26th International Conference on Intelligent User Interfaces

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Organizing fingerings, i.e., choosing which fingers to press on which positions and strings, is a crucial step for playing the violin. As the violin fingering comprises several components, the mapping of a musical phrase to the corresponding fingering arrangement is not unique, and it requires comprehensive musical knowledge for organizing adequate fingerings. In this paper, we study the humanmachine cooperative approach to the generation of violin fingering, aiming to build an intelligent system which can provide multiple generation paths and yield adaptable fingering arrangements. For * The first two authors contributed equally to this research. Yi-Hsin Jen contributed to this research during the period of affiliation with Academia Sinica.

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“…Learning to play a musical instrument is a complex process that requires the acquisition of many interlinked skills such as tone, intonation, note accuracy, timing, rhythm precision, clear articulation, and dynamic variation [1]. An acknowledged important skill is the understanding of rhythm, timing, and rhythm precision.…”

Section: Introductionmentioning

confidence: 99%

“…The word rhythm is used to refer to all of the temporal aspects of a musical work [3]. Two main contexts within which formal musical learning takes place are the classroom, through playing and interacting with a teacher, and at home, practicing alone [1]. From the perspective of a drum kit or percussive instrument, a typical classroom setting involves the teacher and a learner.…”

Section: Introductionmentioning

confidence: 99%

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'1e0a': A Computational Approach to Rhythm Training

Alben,

2021

Preprint

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We present a computational assessment system that promotes the learning of basic rhythmic patterns. The system is capable of generating multiple rhythmic patterns with increasing complexity within various cycle lengths. For a generated rhythm pattern the performance assessment of the learner is carried out through the statistical deviations calculated from the onset detection and temporal assessment of a learner's performance. This is compared with the generated pattern, and their performance accuracy forms the feedback to the learner. The system proceeds to generate a new pattern of increased complexity when performance assessment results are within certain error bounds. The system thus mimics a learner-teacher relationship as the learner progresses in their feedback-based learning. The choice of progression within a cycle for each pattern is determined by a predefined complexity metric. This metric is based on a coded element model for the perceptual processing of sequential stimuli. The model earlier proposed for a sequence of tones and non-tones, is now used for onsets and silences. This system is developed into a webbased application and provides accessibility for learning purposes. Analysis of the performance assessments shows that the complexity metric is indicative of the perceptual processing of rhythm patterns and can be used for rhythm learning.

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Performance Assessment Technologies for the Support of Musical Instrument Learning

Cited by 16 publications

References 31 publications

Quantitative analysis of piano performance proficiency focusing on difference between hands

Quantitative analysis of piano performance proficiency focusing on difference between hands

Positioning Left-hand Movement in Violin Performance: A System and User Study of Fingering Pattern Generation

'1e0a': A Computational Approach to Rhythm Training

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