{"id":"W1552181964","doi":"10.5281/zenodo.40218","title":"Integrating The Cochlea''S Compressive Nonlinearity In The Bayesian Approach For Speech Enhancement","year":2007,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Speech enhancement; Bayesian probability; Estimator; Formant; Speech recognition; Computer science; Distortion (music); Noise reduction; Cochlea; Noise (video); Nonlinear system; Artificial intelligence; Mathematics; Statistics; Vowel; Physics; Telecommunications; Bandwidth (computing)","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":[0.0009675108,0.0006649386,0.0005146465,0.0003247562,0.0002984116,0.0007161751,0.0005802222,0.0009512115,0.002309773],"category_scores_gemma":[0.002860867,0.0003924458,0.0004916743,0.0003189754,0.0005630895,0.001864605,0.001024124,0.001094786,0.000796522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002713763,"about_ca_system_score_gemma":0.0006967026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002204872,"about_ca_topic_score_gemma":0.005749262,"domain_scores_codex":[0.9996318,0.0001159523,0.00001398429,0.00003926511,0.0001667217,0.00003229419],"domain_scores_gemma":[0.9994575,0.00032325,0.00002490657,0.00004347226,0.0001259651,0.00002486729],"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":[0.0004202426,0.0001525645,0.001275214,0.0004397989,0.0001479836,0.0002275089,0.0002447871,0.3712704,0.1055393,0.09515187,0.002566921,0.4225633],"study_design_scores_gemma":[0.00001100511,0.00006117644,0.0004059537,0.00003220647,0.00003209709,0.0001848539,0.00001887766,0.9726218,0.01001976,0.01415661,0.002425033,0.00003062387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006661337,0.0004083264,0.9910331,0.0001857563,0.00003671119,0.00000886389,0.00001984903,0.00007861442,0.001567347],"genre_scores_gemma":[0.3816681,0.002900291,0.603616,0.0002575999,0.0002555472,0.00007150064,0.0001636833,0.0001838118,0.01088338],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002309773,"threshold_uncertainty_score":0.007726967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02210860454435975,"score_gpt":0.2932909277283259,"score_spread":0.2711823231839661,"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."}}