A Machine learning approach to violin bow technique classification: a comparison between IMU and MOCAP systems

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Dalmazzo, David
• dc.contributor.author Tassani, Simone
• dc.contributor.author Ramírez, Rafael,1966-
• dc.date.accessioned 2020-01-09T08:10:56Z
• dc.date.available 2020-01-09T08:10:56Z
• dc.date.issued 2018
• dc.description 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.
• dc.description.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.
• dc.description.sponsorship 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).
• dc.format.mimetype application/pdf
• dc.identifier.citation Dalmazzo D, Tassani S, Ramírez R. A Machine learning approach to violin bow technique classification: a comparison between IMU and MOCAP systems. In: Matthies DJC, Haescher M, Yordanova K, Bieber G, Schröder M, Kirste T, Urban, B, editors. 5th international Workshop on Sensor-based Activity Recognition and Interaction, Proceedings; 2018 Sep 20-21; Berlin, Germany. New York: Association for Computing Machinery; 2018:12. DOI: 10.1145/3266157.3266216
• dc.identifier.doi http://dx.doi.org/10.1145/3266157.3266216
• dc.identifier.isbn 978-1-4503-6487-4
• dc.identifier.uri http://hdl.handle.net/10230/43239
• dc.language.iso eng
• dc.publisher ACM Association for Computer Machinery
• dc.relation.ispartof Matthies DJC, Haescher M, Yordanova K, Bieber G, Schröder M, Kirste T, Urban, B, editors. 5th international Workshop on Sensor-based Activity Recognition and Interaction, Proceedings; 2018 Sep 20-21; Berlin, Germany. New York: Association for Computing Machinery; 2018.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/688269
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2013-48152-C2-2-R
• dc.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
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Gesture
• dc.subject.keyword Machine Learning
• dc.subject.keyword MOCAP
• dc.subject.keyword Myo Armband
• dc.subject.keyword Audio Descriptors
• dc.title A Machine learning approach to violin bow technique classification: a comparison between IMU and MOCAP systems
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion