{"id":"W2000916836","doi":"10.1109/89.902276","title":"An adaptive KLT approach for speech enhancement","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":235,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Speech enhancement; Speech recognition; Noise (video); Computer science; Speech processing; White noise; Additive white Gaussian noise; Residual; Background noise; Colors of noise; Distortion (music); Mathematics; Artificial intelligence; Noise reduction; Algorithm; 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":[0.0004629183,0.0007296977,0.0005198315,0.0007440879,0.0002657289,0.0005898561,0.0006896963,0.0006557532,0.002078975],"category_scores_gemma":[0.0009670551,0.0002483186,0.0006885913,0.0005719202,0.0004379277,0.0008595167,0.0006135628,0.0007517397,0.001308603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003030722,"about_ca_system_score_gemma":0.0004103679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008711552,"about_ca_topic_score_gemma":0.001368324,"domain_scores_codex":[0.9995084,0.00007657913,0.00002993704,0.0001027159,0.0002499982,0.00003239295],"domain_scores_gemma":[0.9996955,0.00009743718,0.00002743288,0.00003528197,0.0001320033,0.00001228741],"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":[0.000199089,0.00007506512,0.0004037103,0.0001660612,0.00006520696,0.0001566409,0.0001320803,0.05082987,0.2024493,0.01043239,0.001615354,0.7334752],"study_design_scores_gemma":[0.00002840018,0.0002158452,0.0008845259,0.00001991648,0.00005633854,0.0006063598,0.00004327236,0.8977051,0.08075202,0.005864569,0.01377593,0.00004776431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001977151,0.0001975784,0.9968891,0.00003264851,0.0000251283,0.0000152254,0.00000878919,0.0002576773,0.0005967661],"genre_scores_gemma":[0.08430077,0.0006371111,0.9094498,0.0001243682,0.00008878113,0.00007807832,0.0001226441,0.0001249397,0.005073483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002078975,"threshold_uncertainty_score":0.006954908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0278643172740081,"score_gpt":0.2735090793971463,"score_spread":0.2456447621231382,"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."}}