The Sound Demixing Challenge 2023 \(\unicode{x2013}\) Cinematic Demixing Track

This paper summarizes the cinematic demixing (CDX) track of the Sound Demixing Challenge 2023 (SDX'23). We provide a comprehensive summary of the challenge setup, detailing the structure of the competition and the datasets used. Especially, we detail CDXDB23, a new hidden dataset constructed fr...

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Bibliographic Details
Published in:arXiv.org 2024-04
Main Authors: Uhlich, Stefan, Fabbro, Giorgio, Hirano, Masato, Takahashi, Shusuke, Wichern, Gordon, Jonathan Le Roux, Chakraborty, Dipam, Mohanty, Sharada, Li, Kai, Luo, Yi, Yu, Jianwei, Gu, Rongzhi, Solovyev, Roman, Stempkovskiy, Alexander, Habruseva, Tatiana, Sukhovei, Mikhail, Mitsufuji, Yuki
Format: Article
Language:English
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Datasets
Demixing
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Summary:This paper summarizes the cinematic demixing (CDX) track of the Sound Demixing Challenge 2023 (SDX'23). We provide a comprehensive summary of the challenge setup, detailing the structure of the competition and the datasets used. Especially, we detail CDXDB23, a new hidden dataset constructed from real movies that was used to rank the submissions. The paper also offers insights into the most successful approaches employed by participants. Compared to the cocktail-fork baseline, the best-performing system trained exclusively on the simulated Divide and Remaster (DnR) dataset achieved an improvement of 1.8 dB in SDR, whereas the top-performing system on the open leaderboard, where any data could be used for training, saw a significant improvement of 5.7 dB. A significant source of this improvement was making the simulated data better match real cinematic audio, which we further investigate in detail.
ISSN:2331-8422