{"id":"W4280644428","doi":"10.5920/jcms.902","title":"Contemporary music genre rhythm generation with machine learning","year":2022,"lang":"en","type":"article","venue":"Journal of Creative Music Systems","topic":"Music Technology and Sound Studies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Centre for Interdisciplinary Research in Music Media and Technology","funders":"","keywords":"Rhythm; Autoencoder; Computer science; Musical; Space (punctuation); Scale (ratio); Artificial intelligence; Artificial neural network; Speech recognition; Visual arts; Art; Aesthetics; Geography","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":[0.001303556,0.000725708,0.0004973249,0.0006600897,0.0002920292,0.001005457,0.001128335,0.0006928385,0.00184379],"category_scores_gemma":[0.003942114,0.0005456211,0.0007042774,0.000601949,0.0005212635,0.001479164,0.0009418906,0.00148656,0.0007297311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007713186,"about_ca_system_score_gemma":0.0004388196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004772005,"about_ca_topic_score_gemma":0.006920968,"domain_scores_codex":[0.9994549,0.000163674,0.00002372252,0.0002352481,0.00008447291,0.00003802053],"domain_scores_gemma":[0.9989478,0.0006425235,0.00005812363,0.0001674351,0.0001480013,0.00003609684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001143262,0.0001708385,0.003528147,0.0001033405,0.0001695728,0.00006743825,0.0001822928,0.5963076,0.01010857,0.007127252,0.001933141,0.3801875],"study_design_scores_gemma":[0.00000290529,0.00001183149,0.000169613,0.000003905299,0.000002960508,0.000007124077,0.000006957463,0.99675,0.0007025921,0.001888139,0.0004505133,0.000003408732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03765177,0.0005899868,0.9574556,0.0002389705,0.00008038965,0.00004383517,0.0001298383,0.001655626,0.002154005],"genre_scores_gemma":[0.6816804,0.0004348972,0.3114019,0.0002296882,0.0001036301,0.0000956981,0.0007928335,0.0002405625,0.005020388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004772005,"threshold_uncertainty_score":0.009488463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05000980582495294,"score_gpt":0.2371609510555783,"score_spread":0.1871511452306254,"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."}}