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For centuries, the history and music of Joseph Franz Haydn and WolfgangAmadeus Mozart have been compared by scholars. Recently, the growing field ofmusic information retrieval (MIR) has offered quantitative analyses tocomplement traditional qualitative analyses of these composers. In this MIRstudy, we classify the composer of Haydn and Mozart string quartets based onthe content of their scores. Our contribution is an interpretable statisticaland machine learning approach that provides high classification accuracies andmusical relevance. We develop novel global features that are automaticallycomputed from symbolic data and informed by musicological Haydn-Mozartcomparative studies, particularly relating to the sonata form. Several of theseproposed features are found to be important for distinguishing between Haydnand Mozart string quartets. Our Bayesian logistic regression model attainsleave-one-out classification accuracies over 84%, higher than prior works andproviding interpretations that could aid in assessing musicological claims.Overall, our work can help expand the longstanding dialogue surrounding Haydnand Mozart and exemplify the benefit of interpretable machine learning in MIR,with potential applications to music generation and classification of otherclassical composers.

Where Does Haydn End and Mozart Begin? Composer Classification of String Quartets