Bowing Gestures Classification in Violin Performance: A Machine Learning Approach
| Autor: | Rafael Ramirez, David Dalmazzo |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
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lcsh:BF1-990 Machine learning computer.software_genre Hidden Markov Model Motion capture Bow strokes 050105 experimental psychology Motion (physics) Violin 03 medical and health sciences 0302 clinical medicine Inertial measurement unit Spiccato Psychology 0501 psychology and cognitive sciences Bracelet Hidden Markov model General Psychology Original Research Sensors business.industry 05 social sciences IMU audio descriptors machine learning lcsh:Psychology Audio descriptors Martelé Artificial intelligence business computer 030217 neurology & neurosurgery technology enhanced learning Technology enhanced learning Gesture |
| Zdroj: | Recercat. Dipósit de la Recerca de Catalunya instname Frontiers in Psychology, Vol 10 (2019) Frontiers in Psychology |
| ISSN: | 1664-1078 |
| DOI: | 10.3389/fpsyg.2019.00344 |
| Popis: | 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. This work has been partly sponsored by the Spanish TIN project TIMUL (TIN 2013-48152-C2-2-R), the European Union Horizon 2020 research and innovation programme under grant agreement No. 688269 (TELMI project), and the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502). |
| Databáze: | OpenAIRE |
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