{"id":"W2122178311","doi":"10.1109/icassp.2004.1325982","title":"Robust adaptive Kalman filtering-based speech enhancement algorithm","year":2004,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Kalman filter; Computer science; Speech enhancement; Autoregressive model; Estimator; Algorithm; Speech recognition; Noise (video); SIGNAL (programming language); Speech processing; A priori and a posteriori; Adaptive filter; Frequency domain; Filter (signal processing); Artificial intelligence; Mathematics; Statistics; 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":[0.0005822953,0.0006480865,0.001012137,0.000417505,0.0004003582,0.0006605998,0.0009597679,0.0008842343,0.002804792],"category_scores_gemma":[0.001141297,0.0003607845,0.0005160118,0.0003582571,0.0003240458,0.0007656964,0.0005778609,0.0007870747,0.001494413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003999818,"about_ca_system_score_gemma":0.0008739592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003311431,"about_ca_topic_score_gemma":0.002360245,"domain_scores_codex":[0.9996421,0.00005271301,0.00002540988,0.0001310834,0.0001134457,0.0000351806],"domain_scores_gemma":[0.9997019,0.0001071921,0.00003829974,0.00002611942,0.0001186491,0.000007795316],"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":[0.0002227227,0.00006010144,0.0006150321,0.0001926295,0.0001069889,0.0001385538,0.0001240542,0.4354857,0.02043279,0.01548019,0.003321872,0.5238194],"study_design_scores_gemma":[0.000019121,0.00004526434,0.0001918219,0.00001300477,0.0000230209,0.00006537645,0.000007445395,0.9897134,0.004795481,0.001701264,0.003411383,0.00001347155],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001764093,0.0002480605,0.9961881,0.00003946647,0.00002862997,0.00002113277,0.00001601078,0.000516969,0.001177576],"genre_scores_gemma":[0.2874189,0.0008978773,0.702036,0.0001716268,0.0001030123,0.000254884,0.0002671305,0.0001332037,0.008717219],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003311431,"threshold_uncertainty_score":0.009382963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02951101023049379,"score_gpt":0.2370080247774188,"score_spread":0.207497014546925,"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."}}