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Title:	A Machine learning approach to violin bow technique classification: a comparison between IMU and MOCAP systems
Author:	Dalmazzo, David; Tassani, Simone; Ramírez, Rafael,1966-
Abstract:	Comunicació presentada a: 5th international Workshop on Sensor-based Activity Recognition and Interaction celebrat el 20 i 21 de setembre de 2018 a Berlin, Alemanya.
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 MOCAP-based 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.
Abstract:	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).
Subject(s):	-Gesture -Machine Learning -MOCAP -Myo Armband -Audio Descriptors
Rights:	© ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 5th international Workshop on Sensor-based Activity Recognition and Interaction. http://doi.acm.org/10.1145/10.1145/3266157.3266216
Document type:	Conference Object Article - Accepted version
Published by:	ACM Association for Computer Machinery
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