{"id":"W2148521837","doi":"10.1109/iscas.2008.4542141","title":"Adaptive wavelet denoising system for speech enhancement","year":2008,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Wavelet; Noise reduction; Computer science; Speech recognition; Speech enhancement; Noise (video); Thresholding; Smoothing; Active noise control; Artificial intelligence; Pattern recognition (psychology); Noise measurement; 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":[0.0004926608,0.000122434,0.0001772259,0.00007381491,0.0002719275,0.00007369618,0.0004669154,0.00004159165,0.000009491946],"category_scores_gemma":[0.00002803886,0.0001028406,0.00008249687,0.0001967014,0.00002824923,0.0003123301,0.000108545,0.00005930353,0.00006911448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009035199,"about_ca_system_score_gemma":0.00007205591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003117199,"about_ca_topic_score_gemma":0.00000122656,"domain_scores_codex":[0.998831,0.00007750924,0.0002126768,0.000334834,0.0002415304,0.0003024818],"domain_scores_gemma":[0.999194,0.0001635116,0.00006073608,0.0003641385,0.0001481435,0.0000694344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001712461,0.0002434152,0.00003367601,0.0001741838,0.0001403223,0.0006369184,0.003529547,0.00006908694,0.113129,0.2186498,0.0198877,0.643335],"study_design_scores_gemma":[0.001101794,0.0003393587,0.00009240412,0.00006431379,0.00001062509,0.0003821539,0.0001126956,0.0800797,0.9112914,0.001553018,0.004614803,0.0003577701],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004075524,0.00008575941,0.9821688,0.0001287654,0.0004086782,0.0002533995,7.466566e-7,0.0001795953,0.01269872],"genre_scores_gemma":[0.2592825,0.000003356968,0.7366911,0.0002415088,0.00009665219,0.00001757661,6.919124e-7,0.000007395707,0.003659138],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7981624,"threshold_uncertainty_score":0.4193716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05072266323307025,"score_gpt":0.281191284732059,"score_spread":0.2304686214989887,"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."}}