{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008723884,0.000522177,0.0003017742,0.003474669,0.0004580757,0.001817738,0.0003240467,0.0006491675,0.008789891],"category_scores_gemma":[0.00797666,0.0001500895,0.0004080377,0.002395134,0.0004242152,0.001148907,0.0006208355,0.0006587291,0.002470278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007237924,"about_ca_system_score_gemma":0.0005663305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006685178,"about_ca_topic_score_gemma":0.00535143,"domain_scores_codex":[0.9992917,0.0001655166,0.00005749919,0.0001535854,0.0002152531,0.0001163598],"domain_scores_gemma":[0.9955364,0.002037556,0.0008109936,0.0002104919,0.001000188,0.0004042427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002361905,0.0004755751,0.596467,0.000215196,0.0001047098,0.0003959831,0.0004076745,0.006766453,0.004739645,0.004026471,0.005661849,0.3805033],"study_design_scores_gemma":[0.00002309695,0.0001442537,0.8679605,0.0001295976,0.00005903703,0.0004847802,0.001061182,0.1145761,0.0026955,0.005041203,0.007780727,0.00004400757],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8669568,0.004124458,0.05131585,0.001721344,0.0003166315,0.0001922571,0.003002189,0.001025607,0.07134496],"genre_scores_gemma":[0.9831257,0.0006389762,0.01017321,0.00006941306,0.000102905,0.00003135021,0.001039956,0.00004093464,0.004777619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008789891,"threshold_uncertainty_score":0.02940512,"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."}}