Abstract

Virtuosity in music performance is often associated with fast, precise, and efficient sound-producing movements. The generation of such highly skilled movements involves complex joint and muscle control by the central nervous system, and depends on the ability to anticipate, segment, and coarticulate motor elements, all within the biomechanical constraints of the human body. When successful, such motor skill should lead to what we characterize as fluency in musical performance. Detecting typical features of fluency could be very useful for technology-enhanced learning systems, assisting and supporting students during their individual practice sessions by giving feedback and helping them to adopt sustainable movement patterns. In this study, we propose to assess fluency in musical performance as the ability to smoothly and efficiently coordinate while accurately performing slow, transitionary, and rapid movements. To this end, the movements of three cello players and three drummers at different levels of skill were recorded with an optical motion capture system, while a wireless electromyography (EMG) system recorded the corresponding muscle activity from relevant landmarks. We analyzed the kinematic and coarticulation characteristics of these recordings separately and then propose a combined model of fluency in musical performance predicting music sophistication. Results suggest that expert performers' movements are characterized by consistently smooth strokes and scaling of muscle phasic coactivation. The explored model of fluency as a function of movement smoothness and coarticulation patterns was shown to be limited by the sample size, but it serves as a proof of concept. Results from this study show the potential of a technology-enhanced objective measure of fluency in musical performance, which could lead to improved practices for aspiring musicians, instructors, and researchers.

Highlights

  • The movements of an expert musician typically appear as smooth and graceful, with highly complex tasks seeming to come at a minimum of effort (Altenmüller and Schneider, 2009; Jørgensen and Hallam, 2008)

  • Our analysis focused on smoothness calculation from the velocity of the right effector in combination with estimating coarticulation patterns as measured by principal component analysis of surface electromyography

  • Since a minimum number of strokes are needed to compute the input matrix for the Principal Component Analysis (PCA), we opted to include both up- and down strokes in order to increase the number of data points

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Summary

Introduction

The movements of an expert musician typically appear as smooth and graceful, with highly complex tasks seeming to come at a minimum of effort (Altenmüller and Schneider, 2009; Jørgensen and Hallam, 2008). This quality of smoothness and flow of movement can be defined as fluency (Whiting et al, 1987; Kerr et al, 2013). In the context of technology enhanced learning, a way to accurately quantify and measure such fluency would be very useful, for instance in automated systems for feedback to music students. Since fluency is relevant for many kinds of motor skills, implementing this in systems for home practice could make it useful for several different kinds of instrument practice

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