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

Dalmazzo, David; Tassani, Simone; Ramírez, Rafael, 1966-

doi:http://dx.doi.org/10.1145/3266157.3266216

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

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

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.

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.