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

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arxiv 2306.09382 v3 pith:PUL7VFAA submitted 2023-06-15 cs.SD cs.LGcs.MMeess.AS

Minseok Kim , Jun Hyung Lee , Soonyoung Jung This is my paper
classification cs.SDcs.LGcs.MMeess.AS
keywords demixingmusicchallengemodelreportsolutionssoundtfc-tdf-unet
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. LLMs Between the Nodes: Community Discovery Beyond Vectors

    cs.SI 2025-07 reject novelty 3.0 of 10

    CommLLM, a two-step graph-to-text plus LLM prompting method, reports high NMI on six small networks, but its evaluation omits standard community-detection baselines and relies on a prompt tuned on one test set.

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