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To model the periodicity of beats, state-of-the-art beat tracking systems use"post-processing trackers" (PPTs) that rely on several empirically determinedglobal assumptions for tempo transition, which work well for music with asteady tempo. For expressive classical music, however, these assumptions can betoo rigid. With two large datasets of Western classical piano music, namely theAligned Scores and Performances (ASAP) dataset and a dataset of Chopin'sMazurkas (Maz-5), we report on experiments showing the failure of existing PPTsto cope with local tempo changes, thus calling for new methods. In this paper,we propose a new local periodicity-based PPT, called predominant localpulse-based dynamic programming (PLPDP) tracking, that allows for more flexibletempo transitions. Specifically, the new PPT incorporates a method called"predominant local pulses" (PLP) in combination with a dynamic programming (DP)component to jointly consider the locally detected periodicity and beatactivation strength at each time instant. Accordingly, PLPDP accounts for thelocal periodicity, rather than relying on a global tempo assumption. Comparedto existing PPTs, PLPDP particularly enhances the recall values at the cost ofa lower precision, resulting in an overall improvement of F1-score for beattracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838).

Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music