{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009955479,0.0001619949,0.0004203161,0.0002581252,0.0007031438,0.00009171703,0.0004471522,0.00004354226,0.00005049007],"category_scores_gemma":[0.00005877826,0.0001173784,0.00008733927,0.0004436525,0.00008163455,0.0003778949,0.0002226637,0.0005674324,0.000003124586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000135581,"about_ca_system_score_gemma":0.000178943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009080807,"about_ca_topic_score_gemma":0.00001005898,"domain_scores_codex":[0.9981096,0.0004851153,0.0004742978,0.0002169597,0.000535709,0.0001783167],"domain_scores_gemma":[0.9985208,0.0001042313,0.0008111928,0.0002376083,0.0002693029,0.00005684807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001151223,0.001565089,0.0649267,0.0004027573,0.005874804,0.0149532,0.1946795,0.08772053,0.009671815,0.2167701,0.3564109,0.04587334],"study_design_scores_gemma":[0.0095615,0.01270947,0.00852849,0.0004662393,0.0002239531,0.0405975,0.04086353,0.2308521,0.0006576875,0.001249025,0.6524438,0.001846733],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5732647,0.01376034,0.3908826,0.000969225,0.002895157,0.0004636532,0.00001007383,0.0001655865,0.01758875],"genre_scores_gemma":[0.9967051,0.00001576637,0.001199438,0.0001733555,0.0003760798,0.00002952543,0.00000281001,0.00001086741,0.001487025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4234405,"threshold_uncertainty_score":0.5408085,"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."}}