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:

 

 Artificial Intelligence

 Nonlinear Time Series Analysis

 Web Research 

 

 Music Technology

 Interactive  Technologies

 Barcelona MedTech

 Natural Language  Processing

 Nonlinear Time Series  Analysis

UbicaLab

Wireless Networking

Educational Technologies

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Back Dalmazzo D, Ramirez R. Air violin: a machine learning approach to fingering gesture recognition. MIE 2017- Proceedings of the 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education

Dalmazzo D, Ramirez R. Air violin: a machine learning approach to fingering gesture recognition. MIE 2017- Proceedings of the 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education

We train and evaluate two machine learning models for predicting fingering in violin performances using motion and EMG sensors integrated in the Myo device. Our aim is twofold: first, provide a fingering recognition model in the context of a gamification virtual violin application where we measure both right hand (i.e. bow) and left hand (i.e. fingering) gestures, and second, implement a tracking system for a computer assisted pedagogical tool for self-regulated learners in high-level music education. Our approach is based on the principle of mapping-by-demonstration in which the model is trained by the performer. We evaluated a model based on Decision Trees and compared it with a Hidden Markovian Model.

Version in Zenodo: http://doi.org/10.5281/zenodo.1193758

Link: http://st.sigchi.org/publications/toc/mie-2017.html

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