{"id":"W3155183176","doi":"10.5430/wje.v11n2p36","title":"Automatic Music Genre Classification and Its Relation with Music Education","year":2021,"lang":"en","type":"article","venue":"World Journal of Education","topic":"Music and Audio Processing","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Mel-frequency cepstrum; Preprocessor; Artificial intelligence; Convolutional neural network; Classifier (UML); Music information retrieval; Relation (database); Artificial neural network; Process (computing); Deep learning; Speech recognition; Machine learning; Pattern recognition (psychology); Natural language processing; Musical; Feature extraction; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003021993,0.0001009972,0.0001359193,0.0002943248,0.0001539799,0.000217992,0.0001792883,0.00003388941,0.00007476463],"category_scores_gemma":[0.00009084537,0.00008727665,0.00003095727,0.0008378524,0.00001837338,0.001268237,0.00002684928,0.0001677533,0.00000872504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001124863,"about_ca_system_score_gemma":0.002244581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001952606,"about_ca_topic_score_gemma":0.00001809303,"domain_scores_codex":[0.9989765,0.00008550713,0.0003622315,0.0001933149,0.0002692873,0.0001131629],"domain_scores_gemma":[0.9983248,0.00005777221,0.0006098894,0.000203838,0.0007048901,0.00009888317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000006285211,0.0003753841,0.002901474,0.000137094,0.00002500744,0.000003076377,0.003637891,0.00003543472,0.007132649,0.03111679,0.008061934,0.946567],"study_design_scores_gemma":[0.001153394,0.000230474,0.884304,0.002807457,0.0002372633,0.002121849,0.00395111,0.04351995,0.006732506,0.02062077,0.03353434,0.0007868154],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9314521,0.004812375,0.04700772,0.009372304,0.001961334,0.0001520744,2.368666e-7,0.00003802515,0.005203853],"genre_scores_gemma":[0.9628257,0.00004609458,0.03413438,0.001050572,0.0003775225,0.000007096113,0.000004971565,0.000008624261,0.001545043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9457802,"threshold_uncertainty_score":0.3981791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03066607284589922,"score_gpt":0.2699774184298279,"score_spread":0.2393113455839287,"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."}}