{"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":"/code/select-norm","entry":"select_norm","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":6,"n_papers_ran":6,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":4,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":1,"ran":0,"unverified":0},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2502.16611","paper":"/paper/target-speaker-extraction-through-comparing","title":"Target Speaker Extraction through Comparing Noisy Positive and Negative Audio Enrollments","date":"2025-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZBang/USEF-TSE","path":"models/model_USEF_SepFormer.py","file_url":"https://github.com/ZBang/USEF-TSE/blob/HEAD/models/model_USEF_SepFormer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"d2378f0385cb8d5c","mcp_get_code":{"code_sha256":"d2378f0385cb8d5c"}},{"arxiv_id":"2312.11825","paper":"/paper/mossformer2-combining-transformer-and-rnn-1","title":"MossFormer2: Combining Transformer and RNN-Free Recurrent Network for Enhanced Time-Domain Monaural Speech Separation","date":null,"month_inferred_from_arxiv_id":"2023-12","title_source":"archive","repo":"modelscope/ClearerVoice-Studio","path":"clearvoice/clearvoice/models/mossformer2_se/mossformer2.py","file_url":"https://github.com/modelscope/ClearerVoice-Studio/blob/HEAD/clearvoice/clearvoice/models/mossformer2_se/mossformer2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7f09be5e5c3deb5d","mcp_get_code":{"code_sha256":"7f09be5e5c3deb5d"}},{"arxiv_id":"2302.11824","paper":"/paper/mossformer-pushing-the-performance-limit-of","title":"MossFormer: Pushing the Performance Limit of Monaural Speech Separation using Gated Single-Head Transformer with Convolution-Augmented Joint Self-Attentions","date":"2023-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"7f09be5e5c3deb5d","mcp_get_code":{"code_sha256":"7f09be5e5c3deb5d"}},{"arxiv_id":"2002.08688","paper":"/paper/an-empirical-study-of-conv-tasnet","title":"An empirical study of Conv-TasNet","date":"2020-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"fd365b69d72fe0c9","mcp_get_code":{"code_sha256":"fd365b69d72fe0c9"}},{"arxiv_id":"1910.06379","paper":"/paper/dual-path-rnn-efficient-long-sequence","title":"Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation","date":"2019-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JusperLee/Dual-Path-RNN-Pytorch","path":"model/model_rnn.py","file_url":"https://github.com/JusperLee/Dual-Path-RNN-Pytorch/blob/HEAD/model/model_rnn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d7a32d1e19ee3c3c","mcp_get_code":{"code_sha256":"d7a32d1e19ee3c3c"}},{"arxiv_id":"1809.07454","paper":"/paper/tasnet-surpassing-ideal-time-frequency","title":"Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation","date":"2018-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JusperLee/Conv-TasNet","path":"Conv_TasNet_Pytorch/Conv_TasNet.py","file_url":"https://github.com/JusperLee/Conv-TasNet/blob/HEAD/Conv_TasNet_Pytorch/Conv_TasNet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fd365b69d72fe0c9","mcp_get_code":{"code_sha256":"fd365b69d72fe0c9"}}]}