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 have relevant datasets, repositories, frameworks and tools of relevance for research and technology transfer initiatives related to knowledge extraction. This section provides an overview on a selection of them and links to download or contact details.
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 
. Below a non-exhaustive list of datasets representative of the research in the Department.
As part of the promotion of the availability of resources, the creation of specific communities in Zenodo has also been promoted, at level of research communities (for instance, MIR and Educational Data Analytics) or MSc programs (for instance, the Master in Sound and Music Computing)
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.