A Machine Learning Approach to Violin Bow Technique Classification

Abstract

Motion Capture (MOCAP) Systems have been used to analyze body motion and postures in biomedicine, sports, rehabilitation, and music. With the aim to compare the precision of low-cost devices for motion tracking (e.g. Myo) with the precision of MOCAP systems in the context of music performance, we recorded MOCAP and Myo data of a top professional violinist executing four fundamental bowing techniques (i.e. Détaché, Martelé, Spiccato and Ricochet). Using the recorded data we applied machine learning techniques to train models to classify the four bowing techniques. Despite intrinsic differences between the MOCAP and low-cost data, the Myo-based classifier resulted in slightly higher accuracy than the MOCAPbased classifier. This result shows that it is possible to develop music-gesture learning applications based on low-cost technology which can be used in home environments for self-learning practitioners.

Evaluation of a Sound Quality Visual Feedback System for Bow Learning Technique in Violin Beginners: An EEG Study

“…The i-Maestro project (Ng and Nesi, 2008) was one of the first steps in that direction offering tools based on gesture analysis and audio processing. More recently the TELMI project has developed tools for providing feedback on timbre quality, pitch and timing accuracy, posture and bowing techniques, and musical expression (Ortega et al, 2017; Dalmazzo et al, 2018; Giraldo et al, 2018; Zacharias et al, 2018). Optical motion capture combined with sensors has also been used to extract bowing parameters from violin performance (Schoonderwaldt and Demoucron, 2009; Deutsch, 2011) allowing to study and compare the motor patterns of professional and student violinists.…”

Multimodal Embodiment Research of Oral Music Traditions: Electromyography in Oud Performance and Education Research of Persian Art Music

“…For this reason, a strong turn towards the study of EMG data has been observed over the last few years in the music domain too [77]-the assumption behind this being a simple, unequivocal proportional relationship between EMG, underlying muscular forces, and effort, one of the presumptions that I wish to critically examine in this paper. In music research, EMG technologies have been used extensively, both as sensors to study expression in musicrelated gestures [57,76,[78][79][80][81][82][83][84] and as actuators offering vibrotactile feedback [85,86] to enhance instrument-learning practices [87] and provide additional multimodal feedback for those with auditory impairments [88]. For instance, a pilot study (reported in [89]) explored the relationship between effort-related EMG data and musical tension in Iannis Xenakis' piano composition 'Evryali', aiming to address expressed concerns by [90] regarding virtuosity, performability, physical exertion, and energy consumption, or 'energetic striving' [85], in the challenging passages of the work, notorious for the difficulty imposed by the dense and complex graphical notation.…”

A study on the simultaneous consideration of two modalities for the recognition of SoundPainting gestures

“…Par défaut, 5 gestes sont reconnus, mais par l'intermédiaire de son SDK, de nouveaux gestes peuvent être appris. Cet appareil est utilisé dans plusieurs travaux relativement récents comme [10,11] pour la performance musicale (violonistes), pour la classification du mouvement des doigts par [32], pour l'analyse de la navigation par geste de la main par [24], pour la réalité virtuelle par [22], pour la création d'un mapping interactif entre les gestes et des sons musicaux [34] etc. Le Myo™ offre plusieurs avantages par rapport aux autres appareils : un coût abordable (environ 250 €), une configuration simple, et la possibilité d'être caché ce qui est très intéressant en terme de performances artistiques [35].…”