Thesis linked to the implementation of the María de Maeztu Strategic Research Program.

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Back David Dalmazzo's visit to Royal College of Music in London to recollect motion and audio data

In the context of Telmi (Technology Enhanced Learning of Musical Instrument Performance) and its associated MdM project (Technology Enhanced Learning for Instrument learning & analytics tools for assessment), where colleges from the Music and Machine Learning Lab directed by Rafael Ramirez (MTG), are developing different approaches to provide a computer-tool to students and teachers in high music education, enhancing the self-practice attention and dedication.

Last Friday 5th of April David Dalmazzo has visited the Royal College of Music in London to continue the process of recollecting motion and audio data from some expert violinist. The main goal of these recordings is to build a Machine Learning (ML) model based on the current techniques such as Long-Short Term Memory (Recurrent Neural Network) with the intention to classify and recognize in real-time classical bow techniques. During this visit, David has recollected data from known performers and new students, in which they were asked to perform examples of Martelé, Staccato, Dètachè, Ricochèt, Spicatto-Sautille, Legato, Tremolo, Collè and Collegno; as well a short classical piece where some of the bow-strokes techniques were applied into the motivic expression.  

The bow techniques are known by violinist as part of their expressive repertoire, very useful when music-cells, motives or phrases need a concrete musical direction or emphasis.   

The related publication is Bowing Gestures Classification in Violin Performance: A Machine Learning Approach, Front. Psychol., 04 March 2019,, and the data will be available in the MTG-Github repository as well in Repovizz (currently being formatted).