{"id":"W2115912807","doi":"10.1109/icassp.2005.1415315","title":"Speech Enhancement Using a Switching Kalman Filter with a Perceptual Post-Filter","year":2006,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"PESQ; Speech recognition; Speech enhancement; Computer science; Kalman filter; Masking (illustration); Filter (signal processing); Artificial intelligence; Noise reduction; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001549267,0.0002145994,0.0001722157,0.0001220625,0.0002310547,0.0004427175,0.0005158719,0.00004639769,0.0002505007],"category_scores_gemma":[0.000005825166,0.0001557613,0.00005421418,0.0002994325,0.0000301046,0.0008847433,0.0002026738,0.0001403941,0.00009012663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007069271,"about_ca_system_score_gemma":0.00009550167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003497827,"about_ca_topic_score_gemma":0.0001177038,"domain_scores_codex":[0.9983422,0.00002518644,0.0002359525,0.0004993002,0.0004079222,0.0004894714],"domain_scores_gemma":[0.9992851,0.00002261093,0.00008593806,0.0004108867,0.000114108,0.00008135721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003107562,0.0002268321,0.002340603,0.00003763969,0.00003067966,0.0001838333,0.001449571,0.0002006423,0.9041766,0.0008477411,0.001200044,0.08927472],"study_design_scores_gemma":[0.0010203,0.0003524391,0.003415835,0.0001766571,0.00002189512,0.0004502298,0.0003141463,0.02324318,0.9668525,0.001150845,0.002162777,0.0008391597],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4807381,0.00003089405,0.511302,0.000494596,0.00006788426,0.0000838417,3.230947e-7,0.0001185245,0.007163823],"genre_scores_gemma":[0.6061043,5.252297e-7,0.391468,0.001130751,0.0001346412,0.000003744125,0.000001623183,0.00001177037,0.001144648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1253661,"threshold_uncertainty_score":0.6351761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01505169106201238,"score_gpt":0.2378763032488514,"score_spread":0.222824612186839,"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."}}