From Genre Classification to Rhythm Similarity: Computational and Musicological Insights

Traditionally, the development and validation of computational measures of rhythmic similarity in music relies on proxy classification tasks, often equating rhythm similarity to genre. In this paper, we perform a comprehensive, cross-disciplinary exploration of the classification performance of a state-of-the-art system for rhythm similarity. By synthesizing the methods of quantitative analysis with a musicological perspective, detailed insight is gained into the various facets that affect system behaviour, consisting of three main areas: rhythmic sensitivities of a given feature representation, idiosyncrasies of the data used for evaluation, and the tenuous relationship between rhythmic similarity and genre. Through this study, we provide perspective on gauging the abilities of a computational system beyond classification accuracy, as well as a deeper understanding of system design and evaluation methodology as a musically meaningful exercise.