@book{van kranenburg_daga_2023, title={polifonia-project/pitchcontext: Version 0.1.9 of pitchcontext}, url={https://zenodo.org/record/8020644}, DOI={10.5281/ZENODO.8020644}, abstractNote={Library for melody analysis based on pitch context vectors.}, publisher={Zenodo}, author={Van Kranenburg, Peter and Daga, Enrico}, year={2023}, month={Jun} }
Van Kranenburg, P., & Daga, E. (2023). polifonia-project/pitchcontext: Version 0.1.9 of pitchcontext (Version v0.1.9) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.8020644
pitchcontext
Python module for melody analysis based on pitch context vectors.
Prerequisites:
lilypond installed and in command line path.
convert (ImageMagick) installed and in command line path.
kernfiles and corresponding .json files with melodic features.
The .json files need to be formatted according to the standard of MTCFeatures.
Installation
The latest release of the pitchcontext module can be installed from pypi:
$ pip install pitchcontext
The development version can be installed by cloning the repository and by using the provided pyproject.toml and poetry. In root of the rep do:
$ poetry install
This creates a virtual environment with pitchcontext installed.
Examples
Requires a Python3 environment with both pitchcontext and streamlit installed.
Four examples are provided:
apps/st_dissonance.py
apps/st_novelty.py
apps/st_unharmonicity.py
apps/st_impliedchords.py
To run:
$ streamlit run st_dissonance.py -- -krnpath <path_to_kern_files> -jsonpath <path_to_json_files>
The – is needed to pass the following arguments to the python script.