This repository contains scripts we used for data processing. The raw datasets have to be downloaded from their original sources as they are too large to host here.
- Choral Singing Dataset, put inside the folder
datasets/csd - Esmuc Choir Dataset, put inside the folder
datasets/esmuc
After the datasets are download, follow these steps in order:
- Instructions to set up and run the scripts are found in the respective folders eg.
datasets/[dataset]. - run preprocess_csd_and_esmuc.py, check that now there exists the folder
datasets/combined_processed_dataset. This will be used specifically for Open-Unmix. - run batch_convert_mono_to_stereo_for_bsrnn.py, check that now there exists the folder
datasets/combined_processed_dataset_converted_stereo. This will be used specifically for BSRNN. - [for jukebox] bla bla
Since the 3 models are very different from each other, we have put them individually inside their respective directories in models/model. Inside each directory, please follow the respective README.md
- Our models achieved the following Source-to-Distortion Ratio (SDR) results:
| Soprano | Alto | Tenor | Bass | Average | |
|---|---|---|---|---|---|
| SOTA* | 1.67 | 10.70 | -7.13 | 7.42 | 2.88 |
| Open-Unmix | 4.68 | 3.12 | 2.13 | 1.74 | 2.92 |
| BSRNN | 2.16 | 3.12 | 2.86 | 3.21 | 2.84 |
* P. Chandna, H. Cuesta, D. Petermann, and E. Gómez, “A Deep-Learning Based Framework for Source Separation, Analysis, and Synthesis of Choral Ensembles,” Front. Signal Process., vol. 2, p. 808594, Apr. 2022, doi: 10.3389/frsip.2022.808594.
- Check out some samples of our models' inferences on 4 choir songs: https://smu-my.sharepoint.com/:f:/g/personal/kevano_2020_business_smu_edu_sg/Ek2286izPx5Lkbew-vrgjUEBGDwSNMoahMWTMByRZ9-tzA?e=qj7A4L