用统计嵌入分析披头士早期歌曲，揭示创作风格演变与合作者的趋同
Come Together: Analyzing Popular Songs Through Statistical Embeddings
- 将和弦、旋律等特征转化为向量嵌入，实现对歌曲结构的量化分析
- 发现1962-1966年披头士专辑间存在明显聚类，风格随时间逐渐融合
- 适合音乐数据科学、流行音乐风格研究者，可推广至其他作曲家分析
流行音乐的复杂结构使得传统统计方法难以适用。本文提出基于逻辑主成分分析的嵌入方法，将披头士1962至1966年作品中的和弦、旋律音、和弦与音高转换、旋律轮廓等全局特征转化为向量表示，实现标准多变量分析。通过该嵌入框架，我们探究了各专辑间的聚类关系，追踪了约翰·列侬与保罗·麦卡特尼创作风格的演化轨迹，并检验了两人作品在风格上是否趋于一致或分化。结果表明，两位作曲家的风格随时间呈现显著趋同趋势，验证了嵌入方法在流行音乐结构与风格演化研究中的有效性。
原文摘要 · Abstract (English)
Statistical modeling of popular music presents a unique challenge due to the complexity of song structures, which cannot be easily analyzed using conventional statistical tools. However, recent advances in data science have shown that converting non-standard data objects into real vector-valued embeddings enables meaningful statistical analysis. In this work, we demonstrate an approach based on logistic principal component analysis to construct embeddings from global song features, allowing for standard multivariate analysis. We apply this method to a corpus of Lennon and McCartney songs from 1962-1966, using embeddings derived from chords, melodic notes, chord and pitch transitions, and melodic contours. Our analysis explores how these song embeddings cluster by Beatles album, how songwriting styles evolved over time, and whether Lennon and McCartney's compositions exhibited convergence or divergence. This embedding-based approach offers a powerful framework for statistically examining musical structure and stylistic development in popular music.
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