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The Sound Demixing Challenge 2023 – Cinematic Demixing Track

Authors :
Stefan Uhlich
Giorgio Fabbro
Masato Hirano
Shusuke Takahashi
Gordon Wichern
Jonathan Le Roux
Dipam Chakraborty
Sharada Mohanty
Kai Li
Yi Luo
Jianwei Yu
Rongzhi Gu
Roman Solovyev
Alexander Stempkovskiy
Tatiana Habruseva
Mikhail Sukhovei
Yuki Mitsufuji
Source :
Transactions of the International Society for Music Information Retrieval, Vol 7, Iss 1, Pp 44–62-44–62 (2024)
Publication Year :
2024
Publisher :
Ubiquity Press, 2024.

Abstract

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 major source of this improvement was making the simulated data better match real cinematic audio, which we further investigate in detail.

Details

Language :
English
ISSN :
25143298
Volume :
7
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Transactions of the International Society for Music Information Retrieval
Publication Type :
Academic Journal
Accession number :
edsdoj.4d5fad42081f45e48007f343da8811e3
Document Type :
article
Full Text :
https://doi.org/10.5334/tismir.172