pith. sign in

arxiv: 2306.09382 · v3 · pith:PUL7VFAAnew · submitted 2023-06-15 · 💻 cs.SD · cs.LG· cs.MM· eess.AS

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

Minseok Kim , Jun Hyung Lee , Soonyoung Jung This is my paper
classification 💻 cs.SD cs.LGcs.MMeess.AS
keywords demixingmusicchallengemodelreportsolutionssoundtfc-tdf-unet
0
0 comments X
read the original abstract

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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Diff-VS: Efficient Audio-Aware Diffusion U-Net for Vocals Separation

    eess.AS 2026-04 unverdicted novelty 6.0

    Diff-VS is an efficient audio-aware diffusion U-Net for vocal separation that matches discriminative baselines on objective metrics while achieving state-of-the-art perceptual quality via proxy measures.

  2. A Knowledge-Driven Approach to Target Speech Extraction in the Presence of Background Sound Effects for Cinematic Audio Source Separation (CASS)

    eess.AS 2026-04 unverdicted novelty 4.0

    Detecting manners of articulation and adding them as knowledge features improves target speech extraction in cinematic audio with background sounds.