Studying Large Plainchant Corpora Using chant21

This repository contains all code for the two case studies in the paper 'Studying Large Plainchant Corpora Using chant21' (pdf) presented at the Digital Libraries for Musicology 2020 conference. In that paper we also present the CantusCorpus, GregobaseCorpus and the Python package Chant21.

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Main findings. The first case study we discuss in the paper shows that melodic contours of phrases in the GregoBaseCorpus are on average arch shaped, confirming the so-called melodic arch hypothesis. The second case study addresses a particular problem in chant scholarship: the connection between differentiae at the end of psalms, and the opening of the repeated antiphon. Our analyses of the CantusCorpus suggest that this relation differs across modes, and is fairly predictable, but not as predictable as the differentiae themselves.

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Repository structure

• data/phrase-contours: contains all phrase contours used in the first case study, including all random baselines, and the 3000 contour subsets. The contours are semi-normalized: we normalized the duration to 1, and then sampled pitches at 50 time points, but have not yet normalized the pitch to mean 0. So the pitch values are simply midi pitches. All contours can be found in CSV files with the following columns:

  • contour_id a unique identifier for the contour
  • song_id the filename/identifier of the chant from which the contour was extracted
  • phrase_num which phrase in the chant
  • phrase_length the length of the phrase in number of notes
  • phrase_duration the duration of the phrase in quarternotes
  • 0...49 (midi) pitch values sampled at 50 equally spaces positions in the phrase.

• notebooks/: contains all the notebooks used to generate the figures

• figures/: the figures in the paper. The graphs were always generated using Jupyter notebooks, but finalized in Affinity Designer.

• src/: contains the code for generating the data (contours and connections).

• tests/: some unittests of the code in the src directory.

• datasets/: This directory should contain the corpora used. We have not included the corpora as they can be easily downloaded: here are the links to CantusCorpus v0.1 and GregoBaseCorpus v0.4. After downloading the datasets, place them in a directory named datasets. It should now have the following structure:

  - datasets/
    - gregobasecorpus
      - gabc
        - 00001.gabc
        - 00002.gabc
        - ..
    - cantuscorpus
      - csv
        - chant.csv
        - feast.csv
        - ..
  

Python setup

You can find the Python version used in .python-version and all dependencies are listed in requirements.txt. If you use pyenv and venv to manage python versions and virtual environments, do the following:

# Install the right python version
pyenv install | cat .python-version

# Create a virtual environment
python -m venv env

# Activate the environment
source env/bin/activate

# Install dependencies
pip install -r requirements.txt

You should then be able to run the Jupyter notebooks:

cd notebooks
jupyter lab

We have tested all this: installing and running all code from scratch worked as expected.

Generating the data

We have already included the data used in the case studies, but for completeness sake, we also document here how to reproduce those datasets from scratch. Please make sure that you have downloaded the corpora and placed them in the directory datasets/, as described above.

The script src/generate_contours.py is used to generate the contours used in case study 1; the script src/generate-differentiae.py generates the differentia-antiphon connections for case study 2. Run them from the root directory as follows

$ source env/bin/activate
$ python -m src.generate_contours.py
$ python -m src.generate_differentiae.py

This regenerates the data/ directory and its contents.

Citation

Bas Cornelissen, Willem Zuidema, and John Ashley Burgoyne. 2020. Studying Large Plainchant Corpora Using chant21. In 7th International Conference on Digital Libraries for Musicology. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3424911.3425514