This repository contains the data and code for the Master Thesis 'Unsupervised segmentation of Gregorian chant melodies for exploring chant modality' created at Faculty of Mathematics and Physics, Charles University. The goal is to explore the Gregorian chant as an oral tradition where singers had to memorize thousands of melodies. We are looking for a system that would explain the memorization phenomenon. Several hypotheses, such as centonization, etc., suggest the melodic system of compounding melodic units together. We know that there are melodic motifs that are often repeated in melodies. Furthermore, the Gregorian chant is based on the modal system, so each melody belongs to one of eight modes. The memorization process could be related to the modal system since it affects many other Gregorian chant aspects, such as repertoire.
As we mentioned, some of the melody motifs are often repeated. The question arises as to what is the cause of this. Therefore, we are solving the issue by statistical unsupervised segmentation methods and we are analyzing the segmentation behaviour from several perspectives. Musicologists have tried to explain and discuss repeated melodic motifs, but no one has tried to use statistical methods that can work with large amount of data.
The work doesn't aim to find the answer to the memorization problem. We want to provide a different perspective - the statistical one - of solving the phenomenon. We hope to get some observations about melodies in the context of modality and to show the direction that musicologists should work on further.
This repository contains source code, experiment results, and the datasets we use for evaluation.
-
src/The folder contains the source code of the models and evaluation metrics we proposed. For more information, see the docs.md.
-
notebooks/The folder contains jupyter notebooks of all our relevant experiments and their outcomes. For more information, see the experiments.md.
-
datasets/The folder contains filtered and preprocessed datasets we use for training and evaluating our unsupervised methods. For more information, see the datasets.md.
-
bert/The folder contains the necessary files for the BERT pretraining and the following training process. For more information, see the docs.md.
-
final_segmentations/The folder contains the final segmentations generated by the NHPYLMModes model on all chants included in our antiphon, responsory, and antiphons-without-differentiae (no4_antiphons) datasets.
- choose the experiment from notebooks (or measure a new one) and move the notebook to the root directory (experiments.md)
- choose the dataset (antiphons, no4antiphons, or responsories) and unzip the dataset into the root directory (datasets.md)
- run the experiment (docs.md)