{"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/butter-bandpass-filter","entry":"butter_bandpass_filter","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":8,"n_papers_ran":5,"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":7,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":3,"ran":1,"unverified":3},"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":"2406.12998","paper":"/paper/articulatory-encodec-vocal-tract-kinematics","title":"Coding Speech through Vocal Tract Kinematics","date":"2024-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"berkeley-speech-group/speech-articulatory-coding","path":"sparc/inversion.py","file_url":"https://github.com/berkeley-speech-group/speech-articulatory-coding/blob/HEAD/sparc/inversion.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d68ab8febcefd716","mcp_get_code":{"code_sha256":"d68ab8febcefd716"}},{"arxiv_id":"2403.07815","paper":"/paper/chronos-learning-the-language-of-time-series","title":"Chronos: Learning the Language of Time Series","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mobile-sensing-and-ubicomp-laboratory/normwear","path":"modules/signal_preprocess.py","file_url":"https://github.com/mobile-sensing-and-ubicomp-laboratory/normwear/blob/HEAD/modules/signal_preprocess.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"98c7e5db66c62dee","mcp_get_code":{"code_sha256":"98c7e5db66c62dee"}},{"arxiv_id":"2401.14444","paper":"/paper/icassp-2024-speech-signal-improvement","title":"ICASSP 2024 Speech Signal Improvement Challenge","date":"2024-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/sig-challenge","path":"ICASSP2024/demo_synthesizer/synthesizer.py","file_url":"https://github.com/microsoft/sig-challenge/blob/HEAD/ICASSP2024/demo_synthesizer/synthesizer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6e16ffc0f5202e47","mcp_get_code":{"code_sha256":"6e16ffc0f5202e47"}},{"arxiv_id":"2110.03536","paper":"/paper/prototype-learning-for-interpretable","title":"Prototype Learning for Interpretable Respiratory Sound Analysis","date":null,"month_inferred_from_arxiv_id":"2021-10","title_source":"archive","repo":"L3S/PrototypeSound","path":"preprocessing/preprocessing.py","file_url":"https://github.com/L3S/PrototypeSound/blob/HEAD/preprocessing/preprocessing.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"30f5b8f7874d2151","mcp_get_code":{"code_sha256":"30f5b8f7874d2151"}},{"arxiv_id":"2011.00196","paper":"/paper/respirenet-a-deep-neural-network-for","title":"RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting","date":"2020-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/RespireNet","path":"utils.py","file_url":"https://github.com/microsoft/RespireNet/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0adc48d11ded4e7a","mcp_get_code":{"code_sha256":"0adc48d11ded4e7a"}},{"arxiv_id":"1906.01083","paper":"/paper/melnet-a-generative-model-for-audio-in-the","title":"MelNet: A Generative Model for Audio in the Frequency Domain","date":"2019-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YuvalBecker/MelNet","path":"making_mel_Spec.py","file_url":"https://github.com/YuvalBecker/MelNet/blob/HEAD/making_mel_Spec.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ab5d691d9fee133a","mcp_get_code":{"code_sha256":"ab5d691d9fee133a"}},{"arxiv_id":"1807.03418","paper":"/paper/interpreting-and-explaining-deep-neural","title":"AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark","date":"2018-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"soerenab/AudioMNIST","path":"recording_scripts/adjustCuts.py","file_url":"https://github.com/soerenab/AudioMNIST/blob/HEAD/recording_scripts/adjustCuts.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"62a2fe294beb605d","mcp_get_code":{"code_sha256":"62a2fe294beb605d"}},{"arxiv_id":"1708.00853","paper":"/paper/audio-super-resolution-using-neural-networks","title":"Audio Super Resolution using Neural Networks","date":"2017-08-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"ab5d691d9fee133a","mcp_get_code":{"code_sha256":"ab5d691d9fee133a"}}]}