{"id":"W2072369127","doi":"10.1121/1.3508205","title":"Objective and subjective speech quality evaluation of wideband noise reduction algorithms.","year":2010,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Blackberry (Canada); Western University","funders":"","keywords":"PESQ; Computer science; Metric (unit); Wideband; Speech recognition; Benchmarking; Reduction (mathematics); Noise (video); Wideband audio; Noise reduction; Signal-to-noise ratio (imaging); Algorithm; Speech enhancement; Speech coding; Mathematics; Artificial intelligence; Telecommunications; Audio signal; Digital audio","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":[0.004672794,0.0007593659,0.0005144022,0.00120377,0.0002363651,0.0008009471,0.00037282,0.0006581794,0.003299344],"category_scores_gemma":[0.01480035,0.0001480945,0.0004048323,0.0005114453,0.0004321779,0.0007894167,0.0008175919,0.0002894994,0.0009958309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001785379,"about_ca_system_score_gemma":0.0001740713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004756119,"about_ca_topic_score_gemma":0.0009310348,"domain_scores_codex":[0.9959729,0.001299191,0.0004578465,0.0004158432,0.00177398,0.00008030274],"domain_scores_gemma":[0.9867616,0.005092654,0.001623009,0.001054329,0.005017928,0.0004506309],"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":[0.005231367,0.001888519,0.07029751,0.002485748,0.0008123366,0.0004443781,0.001441568,0.01651918,0.4746326,0.0009998117,0.003921277,0.4213258],"study_design_scores_gemma":[0.0005192395,0.01865896,0.5658139,0.0002976366,0.0008706902,0.003021884,0.002314334,0.09168205,0.3017319,0.001597276,0.01282104,0.0006710985],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.790238,0.001683573,0.1934243,0.0001002974,0.00026563,0.001145313,0.001904036,0.0005682275,0.01067071],"genre_scores_gemma":[0.9028146,0.0005618403,0.0874844,0.0001408643,0.0001285897,0.0005391003,0.002369047,0.0001768357,0.005784761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004672794,"threshold_uncertainty_score":0.02471238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02108957310545417,"score_gpt":0.3070187911011025,"score_spread":0.2859292179956483,"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."}}