{"id":"W2155549992","doi":"10.1109/pacrim.1991.160701","title":"Speech enhancement based on Kalman filtering and EM algorithm","year":2002,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Kalman filter; Computer science; Fast Kalman filter; Speech enhancement; Algorithm; Noise (video); Speech recognition; Extended Kalman filter; SIGNAL (programming language); Signal-to-noise ratio (imaging); Invariant extended Kalman filter; Artificial intelligence; Noise reduction; 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":[0.0007663603,0.0006380058,0.0007479756,0.000451923,0.0002965257,0.000669937,0.0005896608,0.0007782206,0.001994539],"category_scores_gemma":[0.001419868,0.0003319229,0.0006925879,0.00042065,0.0003826364,0.001158225,0.0006402633,0.0006626643,0.0009580334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000333179,"about_ca_system_score_gemma":0.0003898548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001805757,"about_ca_topic_score_gemma":0.001586418,"domain_scores_codex":[0.9996365,0.00009538187,0.00002578885,0.00008824932,0.0001314486,0.00002265719],"domain_scores_gemma":[0.9996301,0.0001894523,0.0000301809,0.00003751337,0.0001042838,0.0000084903],"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":[0.000274592,0.00006784402,0.0008043438,0.0003293106,0.0001390575,0.0002497765,0.0002066455,0.2930908,0.03466967,0.03777365,0.003147826,0.6292465],"study_design_scores_gemma":[0.0000152088,0.00005763515,0.0003218213,0.00002027824,0.00003601913,0.0001322438,0.0000135122,0.9785249,0.01050772,0.004225991,0.006121481,0.00002319368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001237753,0.0003365926,0.9969603,0.00003917299,0.00003263905,0.00001211259,0.000007314559,0.0002570974,0.00111684],"genre_scores_gemma":[0.1995052,0.002000869,0.7880242,0.0001199672,0.0001710162,0.000124507,0.0001185588,0.0001224403,0.0098132],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001994539,"threshold_uncertainty_score":0.006672442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01789767810244088,"score_gpt":0.2260928702942446,"score_spread":0.2081951921918037,"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."}}