{"id":"W4252362311","doi":"10.1002/9781119293132.ch6","title":"An Exhaustive Class of Linear Filters","year":2017,"lang":"en","type":"other","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Noise reduction; Distortion (music); Reduction (mathematics); Mathematics; Noise (video); Interference (communication); Filter (signal processing); Wiener filter; Algorithm; Measure (data warehouse); Beamforming; SIGNAL (programming language); Linear filter; Control theory (sociology); Computer science; Mathematical optimization; Statistics; Telecommunications; Artificial intelligence; Data mining; Image (mathematics)","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":[0.0006568953,0.001227587,0.0008821363,0.00116353,0.0007659945,0.002292097,0.0007239984,0.001550042,0.01162731],"category_scores_gemma":[0.002032359,0.00044078,0.0009848971,0.00138222,0.0009271998,0.002355176,0.000941033,0.001744501,0.007402183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005855165,"about_ca_system_score_gemma":0.0007997383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009361374,"about_ca_topic_score_gemma":0.0007863895,"domain_scores_codex":[0.9989818,0.0001300517,0.00006381809,0.0002696942,0.0004512988,0.0001033368],"domain_scores_gemma":[0.9993961,0.0002676125,0.00004952693,0.00009473322,0.0001687268,0.00002324832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007974848,0.00006975362,0.0005201157,0.0007030236,0.00006477702,0.0001780051,0.0001593515,0.01666003,0.008933601,0.3928031,0.01829899,0.5615296],"study_design_scores_gemma":[0.00004639538,0.0002401148,0.001328417,0.000413523,0.00008597786,0.001622068,0.0001346593,0.09675663,0.009453522,0.5249531,0.3648707,0.00009490235],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004681806,0.01541496,0.9238012,0.001170294,0.0004882388,0.00006866376,0.0005296918,0.0007608853,0.05308425],"genre_scores_gemma":[0.2323169,0.07431426,0.5392621,0.00350784,0.004379506,0.0008858318,0.002947788,0.0007187295,0.141667],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01162731,"threshold_uncertainty_score":0.03889728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01993384749142201,"score_gpt":0.2931394267311655,"score_spread":0.2732055792397435,"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."}}