Understanding Violin Players’ Skill Level Based on Motion Capture: a Data-Driven Perspective

Abstract

Learning to play and perform a music instrument is a complex cognitive task, requiring high conscious control and coordination of an impressive number of cognitive and sensorimotor skills. For professional violinists, there exists a physical connection with the instrument allowing the player to continuously manage the sound through sophisticated bowing techniques and fine hand movements. Hence, it is not surprising that great importance in violin training is given to right hand techniques, responsible for most of the sound produced. In this paper, our aim is to understand which motion features can be used to efficiently and effectively distinguish a professional performance from that of a student without exploiting sound-based features. We collected and made freely available a dataset consisting of motion capture recordings of different violinists with different skills performing different exercises covering different pedagogical and technical aspects. We then engineered peculiar features and trained a data-driven classifier to distinguish among two different levels of violinist experience, namely beginners and experts. In accordance with the hierarchy present in the dataset, we study two different scenarios: extrapolation with respect to different exercises and violinists. Furthermore, we study which features are the most predictive ones of the quality of a violinist to corroborate the significance of the results. The results, both in terms of accuracy and insight on the cognitive problem, support the proposal and support the use of the proposed technique as a support tool for students to monitor and enhance their home study and practice.

Musical and Bodily Predictors of Mental Effort in String Quartet Music: An Ecological Pupillometry Study of Performers and Listeners

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Simulation of Gymnastics Performance Based on MEMS Sensor

“…Through artistic performance means to reflect the performance content and theme, through the display of the theme, deeply express its connotation, show the three-dimensional picture, let people experience its deeper influence, let people be on the scene. Through vivid body language to express the cultural connotation, music and clothing are often ignored by choreographers, only catering to the needs of the theme of the performance, to meet the needs of the performance [23]. Gymnastic movements are rich and colorful.…”

Simulation of gymnastics performance based on MEMS sensor

“…Through the means of artistic performance, the content and theme of the performance are reflected, and the content of the theme is deeply expressed through the display of the theme, and the three-dimensional picture is displayed, allowing people to experience its deeper influence and immersive. Through vivid body language to express cultural connotations, music, and clothing are often ignored by the choreographers, only to cater to the needs of the performance theme and to meet the needs of the performance [22]. Gymnastics movements are colorful, different levels have different movements, and different levels of difficulty.…”