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 ...
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.
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