{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004916571,0.0003084897,0.0003214032,0.0006725828,0.0001860797,0.0002765627,0.0009034254,0.0001763302,0.00001456897],"category_scores_gemma":[0.00007233783,0.0002854114,0.00006067427,0.0005236887,0.0004276929,0.0004123356,0.0004262998,0.0003582403,0.00001778847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001133875,"about_ca_system_score_gemma":0.0002450977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009827129,"about_ca_topic_score_gemma":0.0000140093,"domain_scores_codex":[0.9974691,0.00002833929,0.0003963388,0.000992107,0.0007843375,0.0003298021],"domain_scores_gemma":[0.9987193,0.000246885,0.0002764296,0.0004766566,0.0001659133,0.0001148252],"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":[0.000006352787,0.00002115404,0.0000667971,0.00007148778,0.000005284312,0.00002011723,0.000481915,0.003253893,0.0002631355,0.0006396233,0.00003004973,0.9951402],"study_design_scores_gemma":[0.0005898848,0.0003053432,0.001043448,0.001174044,0.00001420195,0.00001658767,6.639133e-7,0.9102532,0.007636076,0.07824682,0.0001269591,0.0005927854],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001990902,0.00003418323,0.9948812,0.0006527044,0.001080963,0.0002226664,0.000005622647,0.00009555407,0.001036214],"genre_scores_gemma":[0.4244117,0.00003609019,0.5669541,0.008002168,0.0003254545,0.00001477319,0.00002523935,0.00005118139,0.0001792882],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9945474,"threshold_uncertainty_score":0.9999598,"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."}}