List of results published directly linked with the projects co-funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Program (MDM-2015-0502).

List of publications acknowledging the funding in Scopus.

The record for each publication will include access to postprints (following the Open Access policy of the program), as well as datasets and software used. Ongoing work with UPF Library and Informatics will improve the interface and automation of the retrieval of this information soon.

The MdM Strategic Research Program has its own community in Zenodo for material available in this repository   as well as at the UPF e-repository   

 

 

Back 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.

 

DOI: 10.1145/3266157.3266216