{"id":"W2152923358","doi":"10.1109/vetec.1990.110294","title":"Acoustic noise suppression using regressive adaptive filtering","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Instituto de Telecomunicações","keywords":"Impulse noise; Active noise control; Noise (video); Microphone; Computer science; Adaptive filter; Colors of noise; Acoustics; Speech recognition; Gradient noise; Noise measurement; Noise floor; Filter (signal processing); Algorithm; Noise reduction; Loudspeaker; Physics; Artificial intelligence; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003443112,0.000237655,0.0001945923,0.0001138289,0.00007862392,0.00002609802,0.000180155,0.00008904323,0.0005895767],"category_scores_gemma":[0.00002884313,0.0002248537,0.00005445974,0.0001463419,0.0000439649,0.0003509285,0.0001014646,0.0002035423,0.00005522821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001437896,"about_ca_system_score_gemma":0.000002301958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005933803,"about_ca_topic_score_gemma":0.000001952624,"domain_scores_codex":[0.9990622,0.00001591195,0.0001987896,0.0002291068,0.0001490532,0.0003449146],"domain_scores_gemma":[0.999473,0.00004881498,0.00003614664,0.0003102513,0.0000411607,0.00009064856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007688087,0.00001707998,0.00002262139,0.00003598243,0.00002334133,0.00005551327,0.0001837502,0.208696,0.7840164,0.0001501877,0.002419828,0.004371632],"study_design_scores_gemma":[0.0001435959,0.00003932526,0.00007356543,0.0001987929,0.00001216667,0.00003251565,0.00004921907,0.7689857,0.2290039,0.000282081,0.0008480986,0.0003310221],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04926742,0.000297775,0.9355509,0.000009674453,0.0002864941,0.0002085538,0.00001526908,0.002222668,0.01214124],"genre_scores_gemma":[0.8605701,0.00006020601,0.13874,0.00001960504,0.00009162429,0.00001906594,0.000001733116,0.00006777142,0.0004298145],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8113027,"threshold_uncertainty_score":0.9169268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04371669583990384,"score_gpt":0.2473298531274677,"score_spread":0.2036131572875638,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}