{"id":"W4362564049","doi":"10.1007/978-3-031-29956-8_27","title":"Musical Genre Recognition Based on Deep Descriptors of Harmony, Instrumentation, and Segments","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Music and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Centre for Interdisciplinary Research in Music Media and Technology","funders":"","keywords":"Overfitting; Computer science; Interpretability; Artificial intelligence; Music information retrieval; Feature selection; Machine learning; Deep learning; Feature (linguistics); Pattern recognition (psychology); Speech recognition; Musical; Artificial neural network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002966676,0.0009282301,0.0009044214,0.001576292,0.0002835982,0.000962477,0.0008204941,0.0005346661,0.00667131],"category_scores_gemma":[0.0004672349,0.0002194283,0.0008379583,0.001344685,0.0002354692,0.000882078,0.0009536225,0.001063038,0.00536379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002644185,"about_ca_system_score_gemma":0.0003417628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00232515,"about_ca_topic_score_gemma":0.006167875,"domain_scores_codex":[0.9998017,0.0000160128,0.0000106651,0.00005984167,0.00006222864,0.00004963917],"domain_scores_gemma":[0.9998086,0.00003809697,0.00001881351,0.00004260673,0.0000585124,0.00003333135],"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":[0.0002433654,0.0001622787,0.001564423,0.0001129998,0.00006634737,0.00008382375,0.00003953305,0.00473262,0.07800984,0.001517218,0.009107486,0.9043601],"study_design_scores_gemma":[0.00007799692,0.0004576973,0.01540457,0.0001120316,0.0001987236,0.0005864118,0.0002362878,0.9093078,0.04537944,0.009695688,0.01846908,0.00007418984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1293388,0.003389812,0.8344386,0.0003592692,0.0008630839,0.000171272,0.003131661,0.00601966,0.02228779],"genre_scores_gemma":[0.5496566,0.002977049,0.3881187,0.0004724345,0.0005407116,0.0001801565,0.01270331,0.0005773801,0.04477368],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00667131,"threshold_uncertainty_score":0.02231777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03432718476478811,"score_gpt":0.2459306679163211,"score_spread":0.211603483151533,"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."}}