Form-NN is a deep learning system (comprised of hybrid Neural Network-Decision Tree architectures, TreeGrad and Bi-LSTM-Tree) trained to perform both form and part/phrase analysis of classical music. The system also provides a new dataset, the Standardized Musical Form and Structure Analysis (SMFSA) Database, that provides the form classification and timestamp-based analysis for 200 unique pieces of classical music.
To find out more, check out the provided research paper:
- Master Thesis: "Deep Learning for Musical Form: Recognition and Analysis" (DOI: 10.13140/RG.2.2.33554.12481)
- Conference Paper: "A Novel Dataset and Deep Learning Benchmark for Classical Music Form Recognition and Analysis" (DOI: 10.5281/zenodo.7416689)
- Also contained in the "PaperAndPresentation" folder is the thesis paper, conference paper (and supplement), and presentation of the research.
- The thesis defense can be watched at https://youtu.be/2ZM5jz5gows.
- The conference paper presentation can be watched at https://youtu.be/zwqqrrwFHjg.
See:
- http://danielszelogowski.com/thesis/demo.php for a live demo of the full prediction system.
- https://github.com/danielathome19/Form-NN/releases for a downloadable local demo of the prediction system.
- https://ismir2022program.ismir.net/poster_152.html for the conference paper, including the poster and presentation.
For data used in my experiments:
- All datasets can be found in Data/MIDI and Labels (with example training data stored in Images), the PDF dataset of analyses is available upon request.
- My most recent pre-trained weights can be found in Weights.
NOTE: these folders should be placed in the same folder as "main.py". For folder existing conflicts, simply merge the directories.
In main.py, the "main" function acts as the controller for the model, where calls to train the model, create a prediction, and all other functions are called. One may also call these functions from an external script ("from main import get_total_duration", etc.).
To choose an operation or series of operations for the model to perform, simply edit the main function before running. Examples of all function calls can be seen commented out within main.
The dataset currently only has a small number of pieces of music fully analyzed by timestamp. If you would like to contribute to this dataset by providing analyses, please contact me with the following:
- What piece(s) you would like to analyze — I can send you the PDF of the sheet music or you can find any of the scores on IMSLP for free.
- Your musical background — the dataset needs to follow standardized analytical conventions; currently, these are based very closely on the analysis techniques presented in Douglass Green's Form in Tonal Music: An Introduction to Analysis, 2nd Ed. ISBN: 0030202868, ISBN13: 9780030202865. You should have at least taken an undergraduate Form and Analysis class or have an equal background in music theory.
- If you need, I can provide a draft of annotation guidelines and/or my own example analyses. This will be added as a document later on when fully prepared.
After you complete the analysis of the sheet music, use the existing label file for the MIDI(s) in the dataset to start labeling the timestamps with your annotations. Feel free to email me with your analyzed sheet music for corrections. Refer to Appendices A and B and Chapter 3 in the thesis as needed.
When you are finished with the analysis, email me the finished analyzed score and label file, or send me the score and push the label file to the repository.
Bugs are tracked using the GitHub Issue Tracker.
Please use the issue tracker for the following purpose:
- To raise a bug request; do include specific details and label it appropriately.
- To suggest any improvements in existing features.
- To suggest new features or structures or applications.
The code is licensed under CC0 License 1.0.
The database was compiled from free and open sources with respect to the original file creators and sequencers. This work is purely for educational and research purposes, and no copyright is claimed on any files contained within the database.
If you use this code for your research, please cite this project as either the thesis (Deep Learning for Musical Form: Recognition and Analysis):
@software{Szelogowski_Form-NN_2022,
author = {Szelogowski, Daniel},
doi = {10.13140/RG.2.2.33554.12481},
month = {Apr},
title = {{Form-NN}},
license = {CC0-1.0},
url = {https://github.com/danielathome19/Form-NN},
version = {1.0.0},
year = {2022}
}or the conference paper (A Novel Dataset and Deep Learning Benchmark for Classical Music Form Recognition and Analysis):
@inproceedings{Szelogowski_Dataset-Deep-Learning-Musical-Form,
title = {A Novel Dataset and Deep Learning Benchmark for Classical Music Form Recognition and Analysis},
doi = {10.5281/zenodo.7416689},
author={Szelogowski, Daniel and Mukherjee, Lopamudra and Whitcomb, Benjamin},
year={2022},
month={Dec},
day={4-8}
pages={900–907},
booktitle = {{Proceedings of the 23rd International Society for Music Information Retrieval Conference}},
publisher = {ISMIR},
address = {Bengaluru, India},
venue = {Bengaluru, India},
url = {https://doi.org/10.5281/zenodo.7416689}
}or the dataset (Standardized Musical Form and Structure Analysis (SMFSA) Database):
@misc{Szelogowski_SMFSA-Dataset-And-Form-NN_2022,
author = {Szelogowski, Daniel},
doi = {10.13140/RG.2.2.33554.12481},
month = {Apr},
title = {{SMFSA-Database-And-Form-NN}},
license = {CC0-1.0},
url = {https://github.com/danielathome19/Form-NN},
year = {2022}
}