Generating Musical Continuations with Repetition

This is the code for reproducing the results from our 2021 SMC paper, "Generating Musical Continuations with Repetition".

If you use it, please cite it:

@inproceedings{Velezdevilla:21,
  title={Generating Musical Continuations with Repetition},
  author={Velez de Villa, Sebastian and McLeod, Andrew and Rohrmeier, Martin},
  booktitle={Sound and Music Computing Conference {(SMC)}},
  year={2021},
  pages={131--138}
}

Running

Here's how to run the continuation programs.

• Clone this repository.
• Download and install abjad, LilyPond (needed by abjad) and pretty_midi, as well as the standard python librairies, such as numpy.
• Download any of the monophonic datasets from MIREX 2019: Patterns for Prediction: small, medium or large.
• In the newly downloaded folders, at the same level as the "prime_csv" folder, add 6 folders: "markov_without_prediction_midi", "markov_without_prediction_csv". "markov_with_prediction_midi", "markov_with_prediction_csv", "markov_with_non_exact_prediction_midi" and "markov_with_non_exact_prediction_csv". These folders are needed for the evaluation code.
• Download the evaluation code, and follow the instructions.
• Copy and paste the content of config_corrected.py into config.py (in the newly downloaded evaluation folder), and modify "OUTPUT_FOLDER", "DATASET_PATH" and "FILENAME_FRAGMENT".
• Edit cs.py (in the evaluation code) to cs_corrected.py (copy and paste the code).
• Run the jupyter notebook Generation/Generation.ipynb.

All the code necessary to find patterns and produce the continuations is available in Generation/utility.py, Generation/simple_first_order_mm.py, Generation/string_based.py and Generation/translation_based.py.

License

See the LICENSE file for license rights and limitations (MIT).