Paper year
2026
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Jangdan as Downbeats: Rhythm-Aware Tracking for Expressive Vocal Energy and Tempo Analysis in Korean Pansori
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Abstract
Computational musicology and music information retrieval research on Korean Pansori requires reliable analysis of vocal energy and tempo variation across rhythmic patterns known as jangdan. In this work, a jangdan is treated as a downbeat period: analogous to downbeats in Western music, it denotes both a rhythmic pattern type and the temporal span between two consecutive downbeats. Under this formulation, jangdan tracking is equivalent to downbeat tracking, allowing conventional downbeat-tracking methods to be directly applied to Pansori. Downbeat tracking in Pansori is challenging due to expressive rhythmic cycles, flexible tempi, and sparse accompaniment, which limit the generalization of systems trained on Western music. This paper proposes a rhythm-pattern-aware downbeat (i.e., jangdan) tracking framework based on offline and online Temporal Convolutional Networks (TCNs) and RoFormer-based models. A jangdan-aware Dynamic Bayesian Network (DBN) constrains minimum and maximum downbeat intervals using prior rhythmic knowledge. Using 22.4 h of annotated Pansori recordings, the proposed approach consistently outperforms general-purpose downbeat trackers across all jangdan patterns, with the offline RoFormer and tuned DBN achieving the strongest results. The improved jangdan inference enables detailed analysis of vocal energy and tempo variation. An A-weighted, beat-level vocal energy labeling method reveals characteristic energy contours aligned with specific jangdan cycles, while tempo analysis shows how performers modulate pacing in relation to rhythmic structure. These results demonstrate that identifying jangdan as a downbeat analog and incorporating rhythm-pattern-aware decoding substantially improves downbeat reliability and enables fine-grained analysis of temporal expressivity in Korean Pansori.
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