用概率模型分析圣咏旋律分段，发现记忆效率与调式分类相关。
Gregorian melody, modality, and memory: Segmenting chant with Bayesian nonparametrics
- 基于贝叶斯非参数模型自动分割圣咏旋律片段。
- 分段结果在调式分类上达到当前最佳性能。
- 发现旋律首尾更公式化，利于记忆但不符传统拼贴理论。
长期以来，格列高利圣咏的构成被认为源自若干固定旋律片段的组合，即所谓‘拼贴理论’，虽遭乐学界批评，但圣咏中确实存在频繁重复的旋律片段。由于可能的分段方式数量庞大，仍存在未被发现的合理分段方案支持该理论。近期实证研究显示，分段方法在调式分类任务上优于传统音乐理论特征。受圣咏由修士背诵记忆这一事实启发，本文采用嵌套层次化的皮特曼-约尔语言模型，寻找最优无监督旋律分段。所获分段在调式分类上表现领先；模拟修士从单一礼仪手稿记忆旋律时，发现调式分类能力与记忆效率存在实证关联，并观察到旋律首尾更具规律性，符合表演中调式功能的实际作用。然而，所得分段并非传统意义上的拼贴结构。
原文摘要 · Abstract (English)
The idea that Gregorian melodies are constructed from some vocabulary of segments has long been a part of chant scholarship. This so-called "centonisation" theory has received much musicological criticism, but frequent re-use of certain melodic segments has been observed in chant melodies, and the intractable number of possible segmentations allowed the option that some undiscovered segmentation exists that will yet prove the value of centonisation, and recent empirical results have shown that segmentations can outperform music-theoretical features in mode classification. Inspired by the fact that Gregorian chant was memorised, we search for an optimal unsupervised segmentation of chant melody using nested hierarchical Pitman-Yor language models. The segmentation we find achieves state-of-the-art performance in mode classification. Modeling a monk memorising the melodies from one liturgical manuscript, we then find empirical evidence for the link between mode classification and memory efficiency, and observe more formulaic areas at the beginnings and ends of melodies corresponding to the practical role of modality in performance. However, the resulting segmentations themselves indicate that even such a memory-optimal segmentation is not what is understood as centonisation.
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