{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-sound-demixing-challenge-2023-unicode","title":"The Sound Demixing Challenge 2023 $\\unicode{x2013}$ Cinematic Demixing Track","arxiv_id":"2308.06981","date":"2023-08-14","proceeding":null,"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"],"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 significant source of this improvement was making the simulated data better match real cinematic audio, which we further investigate in detail.","url_abs":"https://arxiv.org/abs/2308.06981v4","url_pdf":"https://arxiv.org/pdf/2308.06981v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-sound-demixing-challenge-2023-unicode","repo_url":"https://github.com/merlresearch/cocktail-fork-separation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2308.06981","atlas_url":"https://app.syntology.ai/?focus=2308.06981","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.06981"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/merlresearch/cocktail-fork-separation","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":2},"by_repo_kind":{"official":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"cb726cf30b2939c9","entry":"mixture_consistency","repo":"merlresearch/cocktail-fork-separation","repo_kind":"official","path":"consistency.py","file_url":"https://github.com/merlresearch/cocktail-fork-separation/blob/HEAD/consistency.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cb726cf30b2939c9"}},{"code_sha256_prefix":"3fe2a25bba764843","entry":"snr_loss","repo":"merlresearch/cocktail-fork-separation","repo_kind":"official","path":"snr.py","file_url":"https://github.com/merlresearch/cocktail-fork-separation/blob/HEAD/snr.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3fe2a25bba764843"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}