In this report, we present our award-winning solutions for the Music Demixing Track of Sound Demixing Challenge 2023. First, we propose TFC-TDF-UNet v3, a time-efficient music source separation model that achieves state-of-the-art results on the MUSDB benchmark. We then give full details regarding our solutions for each Leaderboard, including a loss masking approach for noise-robust training. Code for reproducing model training and final submissions is available at github.com/kuielab/sdx23.
Sound Demixing Challenge 2023 Music Demixing Track Technical Report: TFC-TDF-UNet v3
TFC-TDF-UNet v3 achieves top performance in music source separation on the MUSDB benchmark using a noise-robust loss masking training approach.
- Year
- 2023
- Venue
- arXiv 2023
- Authors
- 3
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- arxiv.org/abs/2306.09382v3ARXIV-DEFAULT
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