  Music tone quality evaluation is generally performed by experts. It could be
subjective and short of consistency and fairness as well as time-consuming. In
this paper we present a new method for identifying the clarinet reed quality by
evaluating tone quality based on the harmonic structure and energy
distribution. We first decouple the quality of reed and clarinet pipe based on
the acoustic harmonics, and discover that the reed quality is strongly relevant
to the even parts of the harmonics. Then we construct a features set consisting
of the even harmonic envelope and the energy distribution of harmonics in
spectrum. The annotated clarinet audio data are recorded from 3 levels of
performers and the tone quality is classified by machine learning. The results
show that our new method for identifying low and medium high tones
significantly outperforms previous methods.
