  For centuries, the history and music of Joseph Franz Haydn and Wolfgang
Amadeus Mozart have been compared by scholars. Recently, the growing field of
music information retrieval (MIR) has offered quantitative analyses to
complement traditional qualitative analyses of these composers. In this MIR
study, we classify the composer of Haydn and Mozart string quartets based on
the content of their scores. Our contribution is an interpretable statistical
and machine learning approach that provides high classification accuracies and
musical relevance. We develop novel global features that are automatically
computed from symbolic data and informed by musicological Haydn-Mozart
comparative studies, particularly relating to the sonata form. Several of these
proposed features are found to be important for distinguishing between Haydn
and Mozart string quartets. Our Bayesian logistic regression model attains
leave-one-out classification accuracies over 84%, higher than prior works and
providing interpretations that could aid in assessing musicological claims.
Overall, our work can help expand the longstanding dialogue surrounding Haydn
and Mozart and exemplify the benefit of interpretable machine learning in MIR,
with potential applications to music generation and classification of other
classical composers.
