  To model the periodicity of beats, state-of-the-art beat tracking systems use
"post-processing trackers" (PPTs) that rely on several empirically determined
global assumptions for tempo transition, which work well for music with a
steady tempo. For expressive classical music, however, these assumptions can be
too rigid. With two large datasets of Western classical piano music, namely the
Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's
Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs
to cope with local tempo changes, thus calling for new methods. In this paper,
we propose a new local periodicity-based PPT, called predominant local
pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible
tempo 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 beat
activation strength at each time instant. Accordingly, PLPDP accounts for the
local periodicity, rather than relying on a global tempo assumption. Compared
to existing PPTs, PLPDP particularly enhances the recall values at the cost of
a lower precision, resulting in an overall improvement of F1-score for beat
tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838).
