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This article is part of the Research Topic

Technology Enhanced Music Learning and Performance

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Psychol. | doi: 10.3389/fpsyg.2019.00344

Bowing Gestures Classification in Violin Performance: A Machine Learning Approach

  • 1Universitat Pompeu Fabra, MTG - Music Technology Group, Spain
  • 2Music Technology Group - Music and Machine Learning Lab (MML), Universidad Pompeu Fabra, Spain

Gestures in music are of paramount importance partly because they are directly linked to musicians' sound and expressiveness. At the same time, current motion capture technologies are capable of detecting body motion/gestures details very accurately.
We present a machine learning approach to automatic violin bow gesture classification based on Hierarchical Hidden Markov Models (HHMM) and motion data. We recorded motion and audio data corresponding to seven representative bow techniques (Détaché, Martelé, Spiccato, Ricochet, Sautillé, Staccato and Bariolage) performed by a professional violin player. We used the commercial Myo device for recording inertial motion information from the right forearm and synchronized it with audio recordings. Data was uploaded into an online public repository.
After extracting features from both the motion and audio data, we trained an HHMM to identify the different bowing techniques automatically. Our model can determine the studied bowing techniques with over 94% accuracy. The results make feasible the application of this work in a practical learning scenario, where violin students can benefit from the real-time feedback provided by the system.

Keywords: Gestures, machine learning, technology enhanced learning, Hidden markov model, IMU, Bracelet, Sensors, Audio descriptors, multimodal, Bow Strokes

Received: 30 Jun 2018; Accepted: 04 Feb 2019.

Edited by:

Masanobu Miura, Hachinohe Institute of Technology, Japan

Reviewed by:

Andrew McPherson, Queen Mary University of London, United Kingdom
Luca Turchet, Queen Mary University of London, United Kingdom  

Copyright: © 2019 Dalmazzo and Ramirez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. David C. Dalmazzo, MTG - Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain,