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

Expert-Novice Level Classification Using Graph Convolutional Network Introducing Confidence-Aware Node-Level Attention Mechanism

In this study, we propose a classification method of expert-novice levels using a graph convolutional network (GCN) with a confidence-aware node-level attention mechanism. In classification using an attention mechanism, highlighted features may not be significant for accurate classification, thereby degrading classification performance. To address this issue, the proposed method introduces a confidence-aware node-level attention mechanism into a spatiotemporal attention GCN (STA-GCN) for the classification of expert-novice levels. Consequently, our method can contrast the attention value of each node on the basis of the confidence measure of the classification, which solves the problem of classification approaches using attention mechanisms and realizes accurate classification. Furthermore, because the expert-novice levels have ordinalities, using a classification model that considers ordinalities improves the classification performance. The proposed method involves a model that minimizes a loss function that considers the ordinalities of classes to be classified. By implementing the above approaches, the expert-novice level classification performance is improved.

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

Music performance can be cognitively and physically demanding. These demands vary across the course of a performance as the content of the music changes. More demanding passages require performers to focus their attention more intensity, or expend greater “mental effort.” To date, it remains unclear what effect different cognitive-motor demands have on performers' mental effort. It is likewise unclear how fluctuations in mental effort compare between performers and perceivers of the same music. We used pupillometry to examine the effects of different cognitive-motor demands on the mental effort used by performers and perceivers of classical string quartet music. We collected pupillometry, motion capture, and audio-video recordings of a string quartet as they performed a rehearsal and concert (for live audience) in our lab. We then collected pupillometry data from a remote sample of musically-trained listeners, who heard the audio recordings (without video) that we captured during the concert. We used a modelling approach to assess the effects of performers' bodily effort (head and arm motion; sound level; performers' ratings of technical difficulty), musical complexity (performers' ratings of harmonic complexity; a score-based measure of harmonic tension), and expressive difficulty (performers' ratings of expressive difficulty) on performers' and listeners' pupil diameters. Our results show stimulating effects of bodily effort and expressive difficulty on performers' pupil diameters, and stimulating effects of expressive difficulty on listeners' pupil diameters. We also observed negative effects of musical complexity on both performers and listeners, and negative effects of performers' bodily effort on listeners, which we suggest may reflect the complex relationships that these features share with other aspects of musical structure. Looking across the concert, we found that both of the quartet violinists (who exchanged places halfway through the concert) showed more dilated pupils during their turns as 1st violinist than when playing as 2nd violinist, suggesting that they experienced greater arousal when “leading” the quartet in the 1st violin role. This study shows how eye tracking and motion capture technologies can be used in combination in an ecological setting to investigate cognitive processing in music performance.

Pursuing Artful Movement Science in Music Performance: Single Subject Motor Analysis With Two Elite Pianists

Piano performance motor learning research requires more “artful” methodologies if it is to meaningfully address music performance as a corporeal art. To date, research has been sparse and it has typically constrained multiple performance variables in order to isolate specific phenomena. This approach has denied the fundamental ethos of music performance which, for elite performers, is an act of interpretation, not mere reproduction. Piano performances are intentionally manipulated for artistic expression. We documented motor movements in the complex task of performance of the first six measures of Chopin’s “Revolutionary” Etude by two anthropometrically different elite pianists. We then discussed their motor strategy selections as influenced by anthropometry and the composer’s musical directives. To quantify the joint angles of the trunk, shoulders, elbows, and wrists, we used a VICON 3 D motion capture system and biomechanical modeling. A Kistler force plate (1 N, Swiss) quantified center of gravity (COG) shifts. Changes in COG and trunk angles had considerable influence on the distal segments of the upper limbs. The shorter pianist used an anticipatory strategy, employing larger shifts in COG and trunk angles to produce dynamic stability as compensation for a smaller stature. Both pianists took advantage of low inertial left shoulder internal rotation and adduction to accommodate large leaps in the music. For the right arm, motor strategizing was confounded by rests in the music. These two cases illustrated, in principle, that expert pianists’ individualized motor behaviors can be explained as compensatory efforts to accommodate both musical goals and anthropometric constraints. Motor learning among piano students can benefit from systematic attention to motor strategies that consider both of these factors.