A multimodal corpus for technology-enhanced learning of violin playing

Volpe, Gualtiero; Kolykhalova, Ksenia; Volta, Erica; Ghisio, Simone; Waddell, George; Alborno, Paolo; Piana, Stefano; Canepa, Corrado; Ramirez-Melendez, Rafael

doi:10.1145/3125571.3125588

Cited by 16 publications

(17 citation statements)

References 6 publications

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“…That the musical skill of rhythmic accuracy scored the highest in technology use is perhaps unsurprising due to the prevalence of metronomes. The low scores for the skills of instrument handling, maintaining good posture, and avoiding injury all speak to the physical aspects of technology, an area that could see further growth soon due to the increasing development of optical and wearable sensors for music performance and corresponding experimental pedagogical applications (e.g., Ng et al, 2007;Van der Linden et al, 2009;Johnson et al, 2010;Volpe et al, 2017). The low score for the skill of good tone or timbre may also speak to the complexity of the construct and a lack of marketready technologies to analyze and develop this skill, although new strides are being made in this area (Himonides, 2009;Giraldo et al, 2017Giraldo et al, , 2019.…”

Section: Discussionmentioning

confidence: 99%

Technology Use and Attitudes in Music Learning

Waddell

Williamon

2019

Front. ICT

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Section: Discussionmentioning

confidence: 99%

Technology Use and Attitudes in Music Learning

Waddell

Williamon

2019

Front. ICT

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“…It enables to assess the degree at which the 3 raters provided their agreement on violin playing skills across stimuli. The results obtained for the whole scale and each sub-scale were ICC=0.81 (audio-video, 95% CI [0.60,0.92], F (19,38) (20,19.1) = 3.59, p < .001), respectively. These high values, falling in the range good and excellent (Cicchetti 1994), indicate that a minimal amount of measurement error was introduced by the 3 independent raters.…”

Section: Analysis and Resultsmentioning

confidence: 94%

“…The project grounds on a tightly coupled interaction between technical and pedagogical partners, to implement new multimodal interaction prototypes for music learning and training, based on state-of-art audio processing, computer vision, and motion capture technologies. To reach its objectives, the TELMI Project built a corpus of multimodal data [19], structured as a collection of exercises to follow the learning path of classical violin programmes. The corpus includes several sources of data, such as motion capture of the performer, of the violin and of the bow, ambient and instrument audio, video, physiological data, (electromyography) and Kinect data.…”

Section: The Telmi Multimodal Archivementioning

confidence: 99%

Automatically measuring biomechanical skills of violin performance

Volta

Mancini

Varni

et al. 2018

Proceedings of the 5th International Conference on Movement and Computing

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This evaluation study explores how automated movement analysis can be used to catch the biomechanical skills needed for a physically accurate violin performance, maximizing efficiency and minimizing injuries. Starting from a previously recorded multimodal dataset, we compute movement features from motion captured data of five violinists performing three violin exercises: octave shift, string crossing, and a Romantic repertoire piece. Three violin teachers were asked to evaluate audio, video, and both audio and video stimuli of the selected exercises. We correlated their ratings with automatically extracted movement features. Whereas these features are purely visual (i.e., they are computed from motion captured data only), we asked teachers to also evaluate audio because it can be considered as the direct translation of movement skills into another modality. In this way, we can also look at possible relations between evaluation of the audio aspects of the performance and biomechanical skills of violin playing. Results show that the proposed movement features can be partially used to measure the biomechanical skills of the violin players to support learning and mitigate the risk of injuries.

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“…The recorded material was post-processed and uploaded into the repoVizz repository and made publicly available 4 . For a comprehensive description of the entire TELMI multimodal archive, refer to [51].…”

Section: Data Descriptionmentioning

confidence: 99%

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

et al. 2020

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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.

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A multimodal corpus for technology-enhanced learning of violin playing

Cited by 16 publications

References 6 publications

Technology Use and Attitudes in Music Learning

Technology Use and Attitudes in Music Learning

Automatically measuring biomechanical skills of violin performance

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

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