Comunicació presentada a: 10th International Workshop on Machine Learning and Music (MML), celebrat a Barcelona (Espanya), el 6 d'octubre de 2017.

The assessment of the sound properties of a performed mu- sical note has been widely studied in the past. Although a consensus exist on what is a good or a bad musical performance, there is not a formal de nition of performance tone quality due to its subjectivity. In this study we present a computational approach for the automatic assess- ment of violin sound production. We investigate the correlations among extracted features from audio performances and the perceptual quality of violin sounds rated by listeners using machine learning techniques. The obtained models are used for implementing a real-time feedback learning 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 inno- vation 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).