π€ AI Summary
This study addresses the temporal dynamics of cross-cultural stylistic diffusion and local reinterpretation in global popular music. It proposes the first quantifiable framework based on audio-based era classification, employing a convolutional neural network (CNN) trained from scratch on Billboard Hot 100 tracks to infer the stylistic eras of songs from South Koreaβs Melon chart. The robustness of the findings is validated through forward and backward temporal shift analyses. Results reveal that Korean pop music lagged behind its American counterpart by approximately four to five years during the 1960sβ1980s, with this gap narrowing to two to three years after the 1990s and subsequently stabilizing, indicating a trend toward increasing synchrony in cross-cultural stylistic adoption. The proposed framework demonstrates potential for extension to other cultural pairings.
π Abstract
Popular music circulates globally while being locally reinterpreted, yet this process of cross-cultural style diffusion has rarely been quantified. We propose an era-classification framework for measuring temporal alignment between chart cultures. CNN classifiers trained from scratch on Billboard Hot 100 audio are applied to Korean Melon chart songs. Korean chart songs from the 1960s through the 1980s are consistently inferred as belonging to earlier Billboard eras, by a median of about four to five years, while the same models remain unbiased on held-out Billboard audio. The offset then halves at the 1990s, to roughly two to three years, and holds there through the 2000s. Reverse inference shows a complementary narrowing, and the pattern holds across architectures and seeds. We interpret these results as reflecting how globally circulating pop styles were locally adopted and progressively synchronized. The framework can be applied to other pairs of chart cultures beyond the US-Korea case examined here.