Sound Demixing Challenge 2023 Music Demixing Track Technical Report: TFC-TDF-UNet v3

15 Jun 2023  ·  Minseok Kim, Jun Hyung Lee, Soonyoung Jung ·

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.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Music Source Separation MUSDB18 TFC-TDF-UNet (v3) SDR (vocals) 9.59 # 3
SDR (drums) 8.44 # 6
SDR (other) 6.86 # 2
SDR (bass) 8.45 # 4
SDR (avg) 8.34 # 4
Music Source Separation MUSDB18-HQ TFC-TDF-UNet (v3) SDR (drums) 8.44 # 7
SDR (bass) 8.45 # 6
SDR (others) 6.86 # 5
SDR (vocals) 9.59 # 6
SDR (avg) 8.34 # 6

Methods