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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10511612" target="_blank" >RIV/00216208:11320/25:10511612 - isvavai.cz</a>

Výsledek na webu

<a href="https://zenodo.org/records/17811449" target="_blank" >https://zenodo.org/records/17811449</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5281/zenodo.17811449" target="_blank" >10.5281/zenodo.17811449</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

The idea that Gregorian melodies are constructed from some vocabulary of segments has long been a part of chant scholarship. This so-called &quot;centonisation&quot; 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

Název v anglickém jazyce

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

Popis výsledku anglicky

The idea that Gregorian melodies are constructed from some vocabulary of segments has long been a part of chant scholarship. This so-called &quot;centonisation&quot; 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

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EH23_025%2F0008691" target="_blank" >EH23_025/0008691: AI zaměřená na člověka pro udržitelnou a adaptabilní společnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 26th Conference of the International Society for Music Information Retrieval

ISBN

978-1-73272-995-7

ISSN

—

e-ISSN

—

Počet stran výsledku

9

Strana od-do

638-646

Název nakladatele

Zenodo

Místo vydání

Geneva, Switzerland

Místo konání akce

Daejeon, Korea

Datum konání akce

21. 9. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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