{"id":"W4312556249","doi":"10.26555/ijain.v8i2.812","title":"Incremental multiclass open-set audio recognition","year":2022,"lang":"en","type":"article","venue":"International Journal of Advances in Intelligent Informatics","topic":"Music and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Machine learning; Set (abstract data type); Incremental learning; Class (philosophy); Open set; Retraining; Support vector machine; Multiclass classification; Test set; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001937258,0.001816015,0.001887638,0.00195705,0.000737708,0.001710979,0.004634766,0.001392885,0.003042466],"category_scores_gemma":[0.007663314,0.0004915304,0.001723792,0.001322484,0.000814865,0.003318704,0.002667212,0.002953471,0.001621775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008974822,"about_ca_system_score_gemma":0.001283186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003714555,"about_ca_topic_score_gemma":0.004779432,"domain_scores_codex":[0.9975491,0.0003140159,0.0001467084,0.0006775525,0.001050988,0.0002616003],"domain_scores_gemma":[0.9948631,0.001548147,0.0003914925,0.001244178,0.00170293,0.0002501538],"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":[0.0002361939,0.0003730665,0.002276891,0.000121182,0.0001154861,0.0001338346,0.0001309513,0.03646651,0.009085611,0.001700688,0.004099416,0.9452602],"study_design_scores_gemma":[0.00001915833,0.0001407978,0.001211682,0.00002095786,0.00004590303,0.0002141895,0.00007208562,0.9754508,0.01580828,0.004743616,0.00223124,0.00004133044],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04907029,0.001158743,0.9361735,0.0002480235,0.0003849005,0.0002825707,0.000295737,0.009301073,0.003085118],"genre_scores_gemma":[0.569485,0.0004834343,0.4226187,0.0003735375,0.0002345905,0.0003286345,0.001778649,0.0003272421,0.004370088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004634766,"threshold_uncertainty_score":0.01024532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04084864497883152,"score_gpt":0.333941201273574,"score_spread":0.2930925562947425,"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."}}