{"id":"W2113131123","doi":"10.1109/tsa.2005.860851","title":"New insights into the noise reduction Wiener filter","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Audio Speech and Language Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":694,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Wiener filter; Noise reduction; Intelligibility (philosophy); Wiener deconvolution; Filter (signal processing); Noise (video); Speech recognition; Distortion (music); Reduction (mathematics); Computer science; Noise measurement; Mathematics; Speech enhancement; Salt-and-pepper noise; Algorithm; Median filter; Artificial intelligence; Telecommunications; Bandwidth (computing); Computer vision; Amplifier","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":[0.0001657081,0.0002381092,0.0001693314,0.0001892041,0.0007634055,0.0005778387,0.0003868194,0.00009583023,0.00003789326],"category_scores_gemma":[0.000005143468,0.0001674614,0.00007262505,0.0006651237,0.00008347175,0.001018905,0.00000605983,0.0003200464,0.00003127495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005238469,"about_ca_system_score_gemma":0.0001424202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000355727,"about_ca_topic_score_gemma":0.0001824135,"domain_scores_codex":[0.9985626,0.00005160524,0.0002537222,0.0004952078,0.0003063999,0.0003304076],"domain_scores_gemma":[0.9993022,0.0000380673,0.000112974,0.0003593782,0.00006711386,0.0001202379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001401589,0.00004818799,0.000004257439,0.00002862643,0.000008541268,0.00002061414,0.004213602,0.0004068674,0.07085279,0.00002221004,0.0004035392,0.9239768],"study_design_scores_gemma":[0.0005037616,0.00006125556,0.00009158882,0.000129763,0.00003011671,0.0001770942,0.0005153624,0.004772837,0.9899597,0.001650658,0.001763024,0.0003448502],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06864345,0.003743509,0.924084,0.001479609,0.0003439104,0.0001365495,5.665891e-7,0.0002860485,0.001282347],"genre_scores_gemma":[0.9183864,0.00005285643,0.07770721,0.0004332244,0.0003362878,0.00001524237,0.000001432622,0.00002125681,0.003046067],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9236319,"threshold_uncertainty_score":0.6828877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007253780499054257,"score_gpt":0.2282763972815397,"score_spread":0.2210226167824855,"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."}}