{"id":"W3048861846","doi":"10.1109/access.2020.3015227","title":"Open Set Audio Recognition for Multi-Class Classification With Rejection","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Music and Audio Processing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Support vector machine; Probabilistic logic; Artificial intelligence; Pattern recognition (psychology); Set (abstract data type); Class (philosophy); Open set; Classifier (UML); Machine learning; Speech recognition; Data mining; 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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000168991,0.000103629,0.0001250172,0.00003912192,0.0002139333,0.001056337,0.001219593,0.00005074963,0.000009362],"category_scores_gemma":[0.00004300665,0.0000882826,0.00002511292,0.0004203835,0.0000222177,0.002603244,0.0001534347,0.00008603677,0.00002965749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002634045,"about_ca_system_score_gemma":0.0000997952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002807769,"about_ca_topic_score_gemma":0.00002624281,"domain_scores_codex":[0.9990513,0.00003297257,0.0001641291,0.0004500161,0.0001380597,0.0001635678],"domain_scores_gemma":[0.9993182,0.00003731947,0.0001815625,0.0002112031,0.0001705201,0.00008113601],"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":[0.000269979,0.0001798005,0.001476924,0.0003346994,0.00006492488,0.000009098125,0.003019478,0.0005380537,0.01481434,0.001177573,0.05378074,0.9243344],"study_design_scores_gemma":[0.003101984,0.0003411174,0.004796909,0.0001839813,0.00004094068,0.00001672848,0.0001373076,0.8706544,0.085,0.002290405,0.03277562,0.0006605862],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01353149,0.00001156587,0.9782661,0.006127353,0.0002675379,0.0005100713,0.000007747225,0.0001485873,0.001129524],"genre_scores_gemma":[0.9146295,0.000009555852,0.0781088,0.00649952,0.0003165182,0.0002237866,0.00003360249,0.00001894435,0.0001597822],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9236738,"threshold_uncertainty_score":0.9999807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3524426434461592,"score_gpt":0.3858772822141878,"score_spread":0.0334346387680286,"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."}}