Daniel McDonald (University of British Columbia)
Title: 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鈥攊ntonation issues, a lost note, an unpleasant sound鈥攂ut 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. In this research, we use data from the CHARM Mazurka Project鈥攆orty-six professional recordings of Chopin鈥檚 Mazurka Op. 68 No. 3 by consummate artists鈥攚ith the goal of elucidating musically interpretable performance decisions. We focus specifically on each performer鈥檚 use of musical tempo by examining the inter-onset intervals of the note attacks in the recording. To explain these tempo decisions, we develop a switching state space model and estimate it by maximum likelihood combined with prior information gained from music theory and performance practice. We use the estimated parameters to quantitatively describe individual performance decisions and compare recordings. These comparisons suggest methods for informing music instruction, discovering listening preferences, and analyzing performances.
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