Abstract
This study explores the extent to which national music preferences reflect underlying cultural values. We collected long-term popular music data from YouTube Music Charts across 62 countries, encompassing both Western and non-Western regions, and extracted audio embeddings using the CLAP model. To complement these quantitative representations, we generated semantic captions for each track using LP-MusicCaps and GPT-based summarization. Countries were clustered based on contrastive embeddings that highlight deviations from global musical norms. The resulting clusters were projected into a two-dimensional space via t-SNE for visualization and evaluated against cultural zones defined by the World Values Survey (WVS). Statistical analyses, including MANOVA and chi-squared tests, confirmed that music-based clusters exhibit significant alignment with established cultural groupings. Furthermore, residual analysis revealed consistent patterns of overrepresentation, suggesting non-random associations between specific clusters and cultural zones. These findings indicate that national-level music preferences encode meaningful cultural signals and can serve as a proxy for understanding global cultural boundaries.
Abstract (translated)
这项研究探讨了国家音乐偏好在多大程度上反映了深层次的文化价值观。我们从YouTube Music Charts收集了62个国家的长期流行音乐数据,这些国家涵盖了西方和非西方地区,并使用CLAP模型提取了音频嵌入。为了补充这些定量表示,我们利用LP-MusicCaps和基于GPT的摘要生成技术为每首歌曲创建语义描述。根据强调偏离全球音乐规范对比嵌入对各国进行了聚类分析。通过t-SNE方法将得到的聚类投影到二维空间中以进行可视化,并与世界经济合作与发展组织(WVS)定义的文化区域进行评估。统计分析,包括多元方差分析(MANOVA)和卡方检验,确认基于音乐的聚类在很大程度上与已有的文化分组相一致。此外,残差分析揭示了一致性的过度表示模式,表明特定聚类与文化区域之间存在非随机关联。这些发现表明,国家级别的音乐偏好编码了有意义的文化信号,并可作为理解全球文化界限的一个代理指标。
URL
https://arxiv.org/abs/2506.13199