{"id":"W2116379893","doi":"10.1016/j.specom.2007.02.002","title":"On the optimal linear filtering techniques for noise reduction","year":2007,"lang":"en","type":"article","venue":"Speech Communication","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Wiener filter; Noise reduction; Computer science; Speech enhancement; Noise (video); Filter (signal processing); Reduction (mathematics); A priori and a posteriori; Algorithm; Speech recognition; Subspace topology; Signal-to-noise ratio (imaging); Linear filter; Noise measurement; Distortion (music); Mathematics; Artificial intelligence; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001173134,0.001402183,0.0009815847,0.0008088713,0.000490385,0.0009990314,0.000792262,0.001220158,0.003335325],"category_scores_gemma":[0.003970146,0.0006423264,0.000854822,0.001008127,0.001460407,0.001659464,0.001112352,0.0019572,0.001448621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004231758,"about_ca_system_score_gemma":0.0006022941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001887615,"about_ca_topic_score_gemma":0.002010005,"domain_scores_codex":[0.9990395,0.0003204994,0.00006597776,0.0001467548,0.0003528389,0.00007442498],"domain_scores_gemma":[0.9990452,0.0006644959,0.00004579469,0.00008946973,0.0001375787,0.00001746972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000397033,0.0001076552,0.0002613841,0.0005031996,0.0001375088,0.000123425,0.0002302996,0.2013407,0.02585371,0.1905723,0.009630386,0.5708423],"study_design_scores_gemma":[0.00003835849,0.0001094249,0.0002825093,0.00008212713,0.00006641388,0.0001660641,0.00004607432,0.8243694,0.01009291,0.1501891,0.01450646,0.00005104596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001718182,0.002275083,0.993397,0.0001871479,0.0001445371,0.000009447052,0.00002019823,0.00008207761,0.002166193],"genre_scores_gemma":[0.139634,0.01086283,0.8302242,0.0004984492,0.001362369,0.0001547667,0.0002502392,0.0002286111,0.01678472],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003335325,"threshold_uncertainty_score":0.01115775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02789165738821853,"score_gpt":0.3013961836864198,"score_spread":0.2735045262982013,"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."}}