Understanding Feature Importance in Musical Works: Unpacking Predictive Contributions to Cluster Analyses

Abstract

Cluster analysis provides insight into musical patterns in composition, performance, and perception. Despite its wide adoption in music research, understanding how specific features affect clustering solutions remains challenging. For example, features such as mode (i.e., major/minor), timing, signal amplitude, and pitch are often intercorrelated, making it difficult to understand their specific role within different clusters. To demonstrate how accumulated local effects (ALEs) can help with this challenge, here we analyze 48 excerpts from complete sets of preludes by Bach and Chopin, showing how specific features contribute to two- and three-cluster analyses. These exploratory analyses reveal that ALEs can identify salient or subtle data patterns from cluster analyses by tracking how changes in features affect cluster membership. We explore these insights in visualizations quantifying feature importance and an interactive companion application ( https://maplelab.net/feature-importance/ ) featuring the analyzed audio. Following a demonstration of this method, we suggest how it can be applied to explore topics of interest to researchers in music information retrieval, empirical musicology, and music cognition alike.