{"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/speech-separation-using-an-asynchronous-fully-1","title":"Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network","arxiv_id":"2112.02321","date":"2021-12-04","proceeding":null,"authors":["Xiaolin Hu","Kai Li","Weiyi Zhang","Yi Luo","Jean-Marie Lemercier","Timo Gerkmann"],"abstract":"Recent advances in the design of neural network architectures, in particular those specialized in modeling sequences, have provided significant improvements in speech separation performance. In this work, we propose to use a bio-inspired architecture called Fully Recurrent Convolutional Neural Network (FRCNN) to solve the separation task. This model contains bottom-up, top-down and lateral connections to fuse information processed at various time-scales represented by \\textit{stages}. In contrast to the traditional approach updating stages in parallel, we propose to first update the stages one by one in the bottom-up direction, then fuse information from adjacent stages simultaneously and finally fuse information from all stages to the bottom stage together. Experiments showed that this asynchronous updating scheme achieved significantly better results with much fewer parameters than the traditional synchronous updating scheme. In addition, the proposed model achieved good balance between speech separation accuracy and computational efficiency as compared to other state-of-the-art models on three benchmark datasets.","url_abs":"https://arxiv.org/abs/2112.02321v1","url_pdf":"https://arxiv.org/pdf/2112.02321v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"speech-separation-using-an-asynchronous-fully-1","repo_url":"https://github.com/JusperLee/AFRCNN-For-Speech-Separation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2112.02321","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.02321"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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/JusperLee/AFRCNN-For-Speech-Separation","reach":null}],"summary":{"ran":4,"unverified":3},"by_repo_kind":{"official":{"samples":7,"ran":4,"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":"c1578c0dbb1d9850","entry":"ConvNormAct","repo":"JusperLee/AFRCNN-For-Speech-Separation","repo_kind":"official","path":"AFRCNN.py","file_url":"https://github.com/JusperLee/AFRCNN-For-Speech-Separation/blob/HEAD/AFRCNN.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c1578c0dbb1d9850"}},{"code_sha256_prefix":"22f00dae6644522b","entry":"DilatedConvNorm","repo":"JusperLee/AFRCNN-For-Speech-Separation","repo_kind":"official","path":"AFRCNN.py","file_url":"https://github.com/JusperLee/AFRCNN-For-Speech-Separation/blob/HEAD/AFRCNN.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"22f00dae6644522b"}},{"code_sha256_prefix":"1db9a6291506bfe8","entry":"GlobLN","repo":"JusperLee/AFRCNN-For-Speech-Separation","repo_kind":"official","path":"AFRCNN.py","file_url":"https://github.com/JusperLee/AFRCNN-For-Speech-Separation/blob/HEAD/AFRCNN.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1db9a6291506bfe8"}},{"code_sha256_prefix":"558fb9eaf252d5ee","entry":"_LayerNorm","repo":"JusperLee/AFRCNN-For-Speech-Separation","repo_kind":"official","path":"AFRCNN.py","file_url":"https://github.com/JusperLee/AFRCNN-For-Speech-Separation/blob/HEAD/AFRCNN.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":"558fb9eaf252d5ee"}},{"code_sha256_prefix":"c50dc8c04c059cd6","entry":"AFRCNN","repo":"JusperLee/AFRCNN-For-Speech-Separation","repo_kind":"official","path":"AFRCNN.py","file_url":"https://github.com/JusperLee/AFRCNN-For-Speech-Separation/blob/HEAD/AFRCNN.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c50dc8c04c059cd6"}},{"code_sha256_prefix":"42619f7bfd90e66e","entry":"Blocks","repo":"JusperLee/AFRCNN-For-Speech-Separation","repo_kind":"official","path":"AFRCNN.py","file_url":"https://github.com/JusperLee/AFRCNN-For-Speech-Separation/blob/HEAD/AFRCNN.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"42619f7bfd90e66e"}},{"code_sha256_prefix":"959283a4e6644323","entry":"Recurrent","repo":"JusperLee/AFRCNN-For-Speech-Separation","repo_kind":"official","path":"AFRCNN.py","file_url":"https://github.com/JusperLee/AFRCNN-For-Speech-Separation/blob/HEAD/AFRCNN.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"959283a4e6644323"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}