Markov-switching State Space Models for Uncovering Musical Interpretation

Abstract

For concertgoers, musical interpretation is the most important factor in determining whether or not we enjoy a classical performance. Every performance includes mistakes-intonation issues, a lost note, an unpleasant sound-but these are all easily forgotten (or unnoticed) when a performer engages her audience, imbuing a piece with novel emotional content beyond the vague instructions inscribed on the printed page. While music teachers use imagery or heuristic guidelines to motivate interpretive decisions, combining these vague instructions to create a convincing performance remains the domain of the performer, subject to the whims of the moment, technical fluency, and taste. In this research, we use data from the CHARM Mazurka Projectforty-six professional recordings of Chopin's Mazurka Op. 63 No. 3 by consumate artists-with the goal of elucidating musically interpretable performance decisions. Using information on the inter-onset intervals of the note attacks in the recordings, we apply functional data analysis techniques enriched with prior information gained from music theory to discover relevant features and perform hierarchical clustering. The resulting clusters suggest methods for informing music instruction, discovering listening preferences, and analyzing performances.