Research Article

Quantitative Evaluation of Stylistic Consistency and Acoustic Evolution: A Case Study of Jay Chou's Career from 2000 to 2026

Haowen ChenBeijing Normal-Hong Kong Baptist University*

* Corresponding author: [email protected]

Abstract

Computational musicology provides a quantitative approach for examining musical style beyond subjective listening impressions. In Mandopop studies, however, long-term acoustic analysis of individual artists remains relatively limited. This study quantitatively examines the acoustic evolution and stylistic continuity of Jay Chou's studio albums from 2000 to 2026. A corpus of 175 locally stored tracks from 16 studio albums was analyzed through digital signal processing. A 27-dimensional analytical feature set was constructed from rhythmic, spectral, dynamic, and timbral descriptors extracted with librosa. Descriptive statistics, statistical comparison tests, Principal Component Analysis (PCA), and K-means clustering were utilized to evaluate whether works released after Jay Chou's Bedtime Stories form an acoustically distinct late-career stage. The results do not support a clear binary rupture between pre- and post-Bedtime Stories works. K-means clustering shows a near-balanced distribution across clusters in both stages, suggesting substantial cross-period overlap. However, significance tests reveal localized differences in selected spectral-textural features, particularly zero-crossing rate and spectral-centroid variation. Album-level PCA further indicates that Children of the Sun occupies a position distinct from Greatest Works of Art and closer to several earlier or mid-career albums, suggesting partial acoustic repositioning rather than a straightforward continuation of recent production patterns. Overall, the findings suggest that Jay Chou's late-career style is better understood as a synthesis of broad acoustic continuity and localized sonic adjustment rather than a definitive stylistic break.

Keywords: Jay Chou; computational musicology; acoustic evolution; PCA; K-means clustering
Published: June 15, 2026
DOI: 10.54254/2753-7064/2026.BJ34570
Volume: CHR Vol.110
pp. 150-155
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