Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music
Ching-Yu Chiu, Meinard M\"uller, Matthew E. P. Davies, Alvin Wen-Yu, Su, and Yi-Hsuan Yang

TL;DR
This paper introduces a novel beat tracking method for expressive classical piano music that effectively handles local tempo variations by using local periodicity analysis, outperforming existing global assumption-based methods.
Contribution
The paper proposes a new local periodicity-based beat tracking algorithm called PLPDP, which improves tracking accuracy in expressive classical music by considering local tempo changes.
Findings
PLPDP increases F1-score in ASAP dataset from 0.473 to 0.493.
PLPDP increases F1-score in Maz-5 dataset from 0.595 to 0.838.
Existing PPTs struggle with local tempo variations in expressive classical music.
Abstract
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…
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Taxonomy
TopicsMusic and Audio Processing · Music Technology and Sound Studies · Speech and Audio Processing
