Here we provide the accompanying code for our tiny paper
- Lele Liu and Christof Weiss, "Utilizing cross-version consistency for domain adaptation: A case study on music audio,” in Tiny Papers Track at International Conference on Learning Representations (Tiny Papers @ ICLR), May 2024.
We use the MAESTRO Dataset, the Schubert Winterreise Dataset and the Wagner Ring Dataset in our case study.
During feature preparation, we calculate the HCQT spectrogram and binary pianoroll for each of the music performance, with a sampling rate of 22.05 kHz and a hop size of 512.
The feature preparation code can be found HERE.
We provide one example input-output pair of feature in the folder example_precomputed_features.
The teacher model is trained using script experiments/teacher_maestro.py.
Below are the scripts for each experiment mentioned in our paper:
| Method | Source Dataset | Target Dataset | Script |
|---|---|---|---|
| Sup | -- | Schubert Wagner | experiments/<target_dataset>/supervised.py |
| T | MAESTRO | Schubert, Wagner | experiments/<target_dataset>/teacher.py |
| TS | MAESTRO | Schubert, Wagner | experiments/<target_dataset>/teacher_student.py |
| TSCV | MAESTRO | Schubert, Wagner | experiments/<target_dataset>/teacher_student_cross_version_1.py |
| TSCV2 | MAESTRO | Schubert, Wagner | experiments/<target_dataset>/teacher_student_cross_version_2.py |
We use synctoolbox to calculate the alignment path between different versions. The python environment is the one provided by the toolbox (copied in file environment-synctoolbox.yml). Please use this python environment to run the feature preparation script eature_preparation/prepare_cross_version_alignment.py.
For running the experiments, please use the provided Dockerfile to build the Docker image. You can also pull the docker image by
docker pull cheriell/cross-version-mpe:0.0.2
Please refer to the runme.sh for the whole reproduction pipeline.
For reproducibility, we uploaded the model checkpoints and pre-calculated features for the test sets at:
- Liu, L., & Weiß, C. (2024). Utilizing Cross-Version Consistency for Domain Adaptation: A Case Study on Music Audio (Pretrained Models and Features) (0.0.1). Zenodo. https://doi.org/10.5281/zenodo.10936492