  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 Project---forty-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.
