Enhancing Music Learning with Smart Technologies

Ramírez, Rafael; Canepa, Corrado; Ghisio, Simone; Kolykhalova, Ksenia; Mancini, Maurizio; Volta, Erica; Volpe, Gualtiero; Giraldo, Sergio; Mayor, Oscar; Perez, Alfonso; Waddell, George; Williamon, Aaron

doi:10.1145/3212721.3212886

Cited by 14 publications

(9 citation statements)

References 1 publication

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“…Among the systems and projects that target sound producing movements many have been focusing on violin playing (e.g., Yin et al, 2005; Ng et al, 2007; Schoonderwaldt and Wanderley, 2007; Ng and Nesi, 2008; Van Der Linden et al, 2011; Ramirez et al, 2018). The Digital ViolinTutor system (Yin et al, 2005) aimed at supporting the learner in the home practice situation through a combination of audio, instructor video and 3D animation of the playing movements.…”

Section: Achieving Movement Fluencymentioning

confidence: 99%

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Characterizing Movement Fluency in Musical Performance: Toward a Generic Measure for Technology Enhanced Learning

et al. 2019

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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.

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Section: Achieving Movement Fluencymentioning

confidence: 99%

“…User tests showed some promise, although the effect was only persistent for some of the players post-tests. More recently, the Telmi project (Dalmazzo and Ramirez, 2017; Ramirez et al, 2018; Volta et al, 2018) aims to integrate both kinematic features as well as higher level features including sway and postural tension.…”

Section: Achieving Movement Fluencymentioning

confidence: 99%

Characterizing Movement Fluency in Musical Performance: Toward a Generic Measure for Technology Enhanced Learning

et al. 2019

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“…Learning how to play a musical instrument has always been difficult, as it requires the learner develop a difficult set of skills [1]. Currently, when learning an instrument, a music student will be asked to learn how to read a score and follow instructions from a teacher in a traditional teacher-student model [2,3]. A score is a tool consisting of abstract graphic symbols that do not have an intuitive relationship with the musical note (pitch and duration of a sound) that each one represents [4].…”

Section: Introductionmentioning

confidence: 99%

AR Graphic Representation of Musical Notes for Self-Learning on Guitar

et al. 2019

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Despite being one of the most commonly self-taught instruments, and despite the ready availability of significant amounts of didactic material, the guitar is a challenging instrument to learn. This paper proposes an application based on augmented reality (AR) that is designed to teach beginner students basic musical chords on the guitar, and provides details of the experimental study performed to determine whether the AR methodology produced faster results than traditional one-on-one training with a music teacher. Participants were divided into two groups of the same size. Group 1 consisted of 32 participants who used the AR app to teach themselves guitar, while Group 2, with a further 32 participants, received formal instruction from a music teacher. Results found no differences in learning times between the two groups based on the variables of method and gender. However, participant feedback suggested that there are advantages to the self-taught approach using AR that are worth considering. A system usability scale (SUS) questionnaire was used to measure the usability of the application, obtaining a score of 82.5, which was higher than the average of 68 that indicates an application to be good from a user experience point of view, and satisfied the purpose for which the application was created.

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“…The implementation of the real-time tone quality feedback system is embedded in SkyNote, a general system for automatic assessment of violin performance developed within the scope of the TELMI project (Ramirez et al, 2018 ). SkyNote computes in real time the frame-based relevant audio descriptors and sends them to the tone quality feedback system through packed lists of OSC messages.…”

Section: Resultsmentioning

confidence: 99%

Automatic Assessment of Tone Quality in Violin Music Performance

et al. 2019

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The automatic assessment of music performance has become an area of increasing interest due to the growing number of technology-enhanced music learning systems. In most of these systems, the assessment of musical performance is based on pitch and onset accuracy, but very few pay attention to other important aspects of performance, such as sound quality or timbre. This is particularly true in violin education, where the quality of timbre plays a significant role in the assessment of musical performances. However, obtaining quantifiable criteria for the assessment of timbre quality is challenging, as it relies on consensus among the subjective interpretations of experts. We present an approach to assess the quality of timbre in violin performances using machine learning techniques. We collected audio recordings of several tone qualities and performed perceptual tests to find correlations among different timbre dimensions. We processed the audio recordings to extract acoustic features for training tone-quality models. Correlations among the extracted features were analyzed and feature information for discriminating different timbre qualities were investigated. A real-time feedback system designed for pedagogical use was implemented in which users can train their own timbre models to assess and receive feedback on their performances.

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Enhancing Music Learning with Smart Technologies

Cited by 14 publications

References 1 publication

Characterizing Movement Fluency in Musical Performance: Toward a Generic Measure for Technology Enhanced Learning

Characterizing Movement Fluency in Musical Performance: Toward a Generic Measure for Technology Enhanced Learning

AR Graphic Representation of Musical Notes for Self-Learning on Guitar

Automatic Assessment of Tone Quality in Violin Music Performance

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