This task will focus on music source separation. Participants will submit systems that separate a song into four instruments: vocals, bass, drums, and other (the instrument "other" contains signals of all instruments other than the first three, e.g., guitar or piano).
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# Baselines
We use Open-Unmix for the baseline. Specifically, we provide trained checkpoints for the UMXL model. You can use the baseline by switching to the openunmix-baseline branch on this repository. To test the models locally, you need to install `git-lfs`.
We use the [Open-Unmix](https://github.com/sigsep/open-unmix-pytorch) library for the baseline. Specifically, we provide trained checkpoints for the UMXL model. You can use the baseline by switching to the `openunmix-baseline` branch on this repository. To test the models locally, you need to install `git-lfs`.
When submitting your own models, you need to submit the checkpoints using `git-lfs`. Check the instructions shared in the inference file [here](https://gitlab.aicrowd.com/music-demixing-challenge-2023/mdx-2023-robust-music-separation-starter-kit/-/blob/openunmix-baseline/my_submission/openunmix_separation_model.py)
# How to Test and Debug Locally
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# Dataset
Download the public dataset for this Task using the link below, you'll need to accept the rules of the competition to access the data. The data is same as the well known MUSDB18-HQ dataset and its compressed version.
Download the public dataset for this task using this [link](https://www.aicrowd.com/challenges/music-demixing-challenge-2023/problems/robust-music-separation/dataset_files), you'll need to accept the rules of the competition to access the data. The data is same as the well known MUSDB18-HQ dataset and its compressed version.
