Submission

Submission Summary

Final scores:	SDR_song	SDR_bass	SDR_drums	SDR_other	SDR_vocals
6.513	6.707	6.712	4.816	7.816

Final scores:	SDR_song	SDR_bass	SDR_drums	SDR_other	SDR_vocals
6.581	6.975	6.646	4.962	7.741

Data
- All 203 tracks of the Moises dataset was used for training (no validation split)
- Augmentation
- Random chunking and mixing sources from different tracks ([1])
Model
- A 'multi-source' version of TFC-TDF U-Net[2, 3] with some architectural improvements, including Channel-wise Subband[4]
- Final submission is an ensemble of 3 models with identical architecture and training procedure but with different random seeds
Noise-robust Training
- Leaderboard A: Loss masking
  - Intuitively, data with high training loss is likely to be audio chunks with labelnoise
  - For each training batch, discard (=don't use for weight update) batch elements with higher loss than some quantile
    - ex) only use half of the training batch for each weight update
- Leaderboard B: Loss masking (along temporal dimension)
  - Compared to labelnoise, data with bleeding seemed to vary less in terms of the amount of noise
  - A more fine-grained masking method performed better (discarding temporal bins with high loss)

[1] S. Uhlich, et al., "Improving music source separation based on deep neural networks through data augmentation and network blending", ICASSP 2017.

[2] W. Choi, et al. "Investigating u-nets with various intermediate blocks for spectrogram-based singing voice separation", ISMIR 2020.

[3] M. Kim, et al. “Kuielab-mdx-net: A two-stream neural network for music demixing”, MDX Workshop at ISMIR 2021.

[4] H. Liu, et al. "Channel-wise Subband Input for Better Voice and Accompaniment Separation on High Resolution Music", INTERSPEECH 2020.

Download mdx_AB.zip, which contains

Create a 'ckpts' folder under my_submission. Unzip the downloaded zip file to 'my_submission/ckpts'.
Copy my_submission and requirements.txt to your SDX 2023 Music Demixing Track Starter Kit.
Run submit.sh after configuring my_submission/user_config.py
- for Leaderboard A, set MySeparationModel = A
- for Leaderboard B, set MySeparationModel = B