{"id":"W1979624743","doi":"10.1155/2007/43745","title":"A Supervised Classification Algorithm for Note Onset Detection","year":2006,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Music and Audio Processing","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Hyperparameter; Computer science; Spectrogram; Classifier (UML); Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Machine learning; Supervised learning; Algorithm; Speech recognition","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":[0.000691474,0.0002112829,0.0002225347,0.0003083452,0.000555185,0.000583033,0.0004956214,0.00007836469,0.000006480981],"category_scores_gemma":[0.0000404487,0.0001864106,0.00008625202,0.0006330241,0.00005599543,0.002653205,0.00003092139,0.0004168909,0.00000679597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001827655,"about_ca_system_score_gemma":0.0001629435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001733401,"about_ca_topic_score_gemma":0.000007155907,"domain_scores_codex":[0.9981114,0.00008529086,0.0005524491,0.0004128932,0.0004259293,0.0004120191],"domain_scores_gemma":[0.9989741,0.0001552777,0.0004336587,0.0001384132,0.0002165935,0.00008191616],"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":[0.00002865751,0.00008079455,0.000144508,0.00003834968,0.000001216069,0.00001577864,0.0001111294,0.001529309,0.004626647,0.0001594332,0.00004281857,0.9932213],"study_design_scores_gemma":[0.001354276,0.0002592555,0.001636398,0.000479891,0.000008179122,0.0002715506,0.00007660342,0.9385471,0.01789439,0.0276393,0.01143011,0.0004028834],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005296158,0.002126858,0.9906432,0.0005087313,0.0003390337,0.0001584087,0.000001741269,0.00008771684,0.0008381382],"genre_scores_gemma":[0.8099229,0.00005756064,0.1888507,0.0005264449,0.0005372398,0.00002184887,0.00000245935,0.0000193595,0.00006148159],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9928185,"threshold_uncertainty_score":0.7601603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02042823757870681,"score_gpt":0.2939237268639054,"score_spread":0.2734954892851986,"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."}}