{"id":"W3186202858","doi":"10.1109/access.2021.3096776","title":"Person Identification From Audio Aesthetic","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Music and Audio Processing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Science and Engineering Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Categorization; Identification (biology); Computer science; Set (abstract data type); Preference; Artificial intelligence; Speech recognition; Human–computer interaction; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004365096,0.0005745098,0.0005524513,0.002122207,0.0002239664,0.0005524302,0.0003349482,0.0004961038,0.002853881],"category_scores_gemma":[0.001781479,0.0001229415,0.0003833486,0.0009984486,0.0001834684,0.0006596539,0.0007728309,0.000297468,0.002352424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001980867,"about_ca_system_score_gemma":0.0001650969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006886737,"about_ca_topic_score_gemma":0.001330056,"domain_scores_codex":[0.9993917,0.00006607577,0.00003395883,0.0001424459,0.0002946184,0.00007115569],"domain_scores_gemma":[0.9993548,0.00008765558,0.0001039388,0.0001003002,0.0003089681,0.00004447705],"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":[0.001103239,0.0001718541,0.03237784,0.0003604452,0.0001040876,0.0005673515,0.000196425,0.004195588,0.09936924,0.001111333,0.007083151,0.8533595],"study_design_scores_gemma":[0.00007377622,0.001270247,0.4024664,0.00020789,0.000397168,0.008605963,0.00158156,0.4110431,0.1396662,0.007268097,0.02718865,0.0002310324],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6600712,0.001394309,0.2962201,0.0002679824,0.0003401813,0.0003472404,0.003371012,0.00324695,0.03474103],"genre_scores_gemma":[0.9277524,0.0006072997,0.06140755,0.0001226946,0.00016428,0.0001264224,0.002451806,0.00006807793,0.007299623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002853881,"threshold_uncertainty_score":0.009547174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05110715317271566,"score_gpt":0.2948192681841568,"score_spread":0.2437121150114411,"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."}}