{"id":"W3089869259","doi":"10.1109/iscas45731.2020.9180931","title":"High-Frequency Component Restoration for Kalman Filter Based Speech Enhancement","year":2020,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Concordia University","funders":"","keywords":"Speech enhancement; Kalman filter; Intelligibility (philosophy); Computer science; Speech recognition; Component (thermodynamics); Distortion (music); Artificial intelligence; Noise reduction; Bandwidth (computing); Telecommunications","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":[0.0004696339,0.00052855,0.000346739,0.0002666738,0.0002386336,0.0002710717,0.0004247168,0.0004874683,0.001653619],"category_scores_gemma":[0.0007503065,0.0002085259,0.0003903323,0.0001618023,0.0002563005,0.0005861201,0.0003745259,0.0006146884,0.0006039254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003087655,"about_ca_system_score_gemma":0.0004439976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003585038,"about_ca_topic_score_gemma":0.00595035,"domain_scores_codex":[0.9998086,0.00002809255,0.00001652967,0.0000548655,0.00007402193,0.0000178417],"domain_scores_gemma":[0.9997841,0.0000679454,0.00002381867,0.00002456533,0.00009146925,0.00000822023],"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":[0.0003418559,0.00009205715,0.001293859,0.0002769771,0.00008921634,0.0001472124,0.0001451177,0.1236441,0.1447132,0.005489505,0.001859021,0.7219079],"study_design_scores_gemma":[0.00001674951,0.0001070962,0.001070579,0.00002188869,0.00004671516,0.0001299284,0.00001925648,0.9370809,0.05490685,0.001296471,0.005279138,0.00002447227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006451541,0.0004013246,0.9918261,0.00004030942,0.00005739826,0.00001629801,0.00001800062,0.0004869334,0.0007021581],"genre_scores_gemma":[0.4170389,0.0008803229,0.575332,0.000169946,0.00008974092,0.00007262647,0.0001873319,0.0001178108,0.006111375],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003585038,"threshold_uncertainty_score":0.007128358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03518443090018528,"score_gpt":0.2579488168007478,"score_spread":0.2227643859005626,"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."}}