Giraldo, SergioRamírez, Rafael,1966-Waddell, GeorgeWilliamon, Aaron2019-12-232019-12-232018Giraldo S, Ramírez R, Waddell G, Williamon A. Computational modelling of timbre dimensions for automatic violin tone quality assessment. In: Thoret E, Goodchild M, McAdams S, editors. Timbre 2018: Timbre Is a Many- Splendored Thing. Timbre 2018; 2018 Jul 4-7; Montreal, Canada. Montreal: McGill University; 2018. p. 57-8.978-1-77247-013-0http://hdl.handle.net/10230/43226Automatic assessment of music performance is an open research area widely studied in the past. A vast amount of systems aiming to enhance the learning process of a musical instrument are being developed in the recent years. However, most of the systems focus on the assessment of pitch and onset accuracy, and very few pay attention to tone quality. This is particularly true in violin music education, where although a consensus exist on what is a good or a bad tone quality, there is not a formal definition due to its subjectivity. We present a machine learning approach for the automatic assessment of violin tone quality. We depart from our previous work on the preliminary modelling of several dimensions involving tone quality. Based on recorded examples of tones with different qualities defined and recorded by a professional violinist, we applied machine learning techniques to learn computational models able to evaluate tone quality from extracted audio features. The tone quality models were implemented into a real-time-visual-feedback system.application/pdfeng© July 2018 The Authors and McGill Universityinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess