A Real-time feedback learning tool to visualize sound quality in violin performances

Abstract

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.