{"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/deep-attractor-network-for-single-microphone","title":"Deep attractor network for single-microphone speaker separation","arxiv_id":"1611.08930","date":"2016-11-27","proceeding":null,"authors":["Zhuo Chen","Yi Luo","Nima Mesgarani"],"abstract":"Despite the overwhelming success of deep learning in various speech\nprocessing tasks, the problem of separating simultaneous speakers in a mixture\nremains challenging. Two major difficulties in such systems are the arbitrary\nsource permutation and unknown number of sources in the mixture. We propose a\nnovel deep learning framework for single channel speech separation by creating\nattractor points in high dimensional embedding space of the acoustic signals\nwhich pull together the time-frequency bins corresponding to each source.\nAttractor points in this study are created by finding the centroids of the\nsources in the embedding space, which are subsequently used to determine the\nsimilarity of each bin in the mixture to each source. The network is then\ntrained to minimize the reconstruction error of each source by optimizing the\nembeddings. The proposed model is different from prior works in that it\nimplements an end-to-end training, and it does not depend on the number of\nsources in the mixture. Two strategies are explored in the test time, K-means\nand fixed attractor points, where the latter requires no post-processing and\ncan be implemented in real-time. We evaluated our system on Wall Street Journal\ndataset and show 5.49\\% improvement over the previous state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1611.08930v2","url_pdf":"http://arxiv.org/pdf/1611.08930v2.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":"deep-attractor-network-for-single-microphone","repo_url":"https://github.com/KMASAHIRO/DANet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"speaker-separation","task_name":"Speaker Separation"},{"task_slug":"speech-separation","task_name":"Speech Separation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.08930","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.08930"}},"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/KMASAHIRO/DANet","reach":null}],"summary":{"ran_honours":1},"by_repo_kind":{"listed":{"samples":1,"ran":1,"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":1,"samples":[{"code_sha256_prefix":"01fb40c4a395f84d","entry":"square_root_of_hann","repo":"KMASAHIRO/DANet","repo_kind":"listed","path":"DANet/evaluation.py","file_url":"https://github.com/KMASAHIRO/DANet/blob/HEAD/DANet/evaluation.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"01fb40c4a395f84d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}