Automatic 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.

This work has been partly sponsored by the Spanish TIN project TIMUL (TIN2013-48152-C2-2-R), the European Union Horizon 2020 research and innovation programme under grant agreement No. 688269 (TELMI project), and the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).