Dalmazzo D, Tassani S, Ramírez R. A Machine Learning Approach to Violin Bow Technique Classification: a Comparison Between IMU and MOCAP systems. iWOAR '18 Proceedings of the 5th international Workshop on Sensor-based Activity Recognition and Interaction
We develop a large number of software tools and hosting infrastructures to support the research developed at the Department. We will be detailing in this section the different tools available. You can take a look for the moment at the offer available within the UPF Knowledge Portal, the innovations created in the context of EU projects in the Innovation Radar and the software sections of some of our research groups:
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Artificial Intelligence |
Nonlinear Time Series Analysis |
Web Research |
Music Technology |
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Interactive Technologies |
Barcelona MedTech |
Natural Language Processing |
Nonlinear Time Series Analysis |
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UbicaLab |
Wireless Networking |
Educational Technologies |
Dalmazzo D, Tassani S, Ramírez R. A Machine Learning Approach to Violin Bow Technique Classification: a Comparison Between IMU and MOCAP systems. iWOAR '18 Proceedings of the 5th international Workshop on Sensor-based Activity Recognition and Interaction
Dalmazzo D, Tassani S, Ramírez R. A Machine Learning Approach to Violin Bow Technique Classification: a Comparison Between IMU and MOCAP systems. iWOAR '18 Proceedings of the 5th international Workshop on Sensor-based Activity Recognition and Interaction
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.