Gregorian melody, modality, and memory: Segmenting chant with Bayesian nonparametrics

TL;DR

This paper employs Bayesian nonparametric models to segment Gregorian chant melodies, achieving state-of-the-art mode classification and providing insights into memorization and formulaic structures in chant performance.

Contribution

It introduces an unsupervised Bayesian segmentation method that outperforms previous features in mode classification and offers empirical evidence linking segmentation to memorization strategies.

Findings

State-of-the-art mode classification accuracy

Memory-efficient segmentation correlates with chant structure

Formulaic sections align with practical performance roles

Abstract

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.