{"id":"W4409346471","doi":"10.1609/aaai.v39i2.32138","title":"MIDI-GPT: A Controllable Generative Model for Computer-Assisted Multitrack Music Composition","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Music Technology and Sound Studies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Council for the Arts","keywords":"MIDI; Composition (language); Generative grammar; Musical composition; Computer science; Computer music; Artificial intelligence; Art; Visual arts; Musical; Literature","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009797002,0.0007416639,0.0004872864,0.0005248862,0.000498248,0.00123513,0.00222906,0.001064084,0.01057392],"category_scores_gemma":[0.002744318,0.0006796806,0.001438657,0.0003672937,0.0008046217,0.001046501,0.002220205,0.001490112,0.003535755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006672538,"about_ca_system_score_gemma":0.0009364354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002306259,"about_ca_topic_score_gemma":0.004608494,"domain_scores_codex":[0.9995978,0.00008546734,0.00002147095,0.0001279991,0.0001347099,0.00003258001],"domain_scores_gemma":[0.9995127,0.0002464351,0.00002706056,0.0001155815,0.00005210625,0.00004619749],"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":[0.0005251713,0.0003496333,0.003676307,0.0004222364,0.0001650169,0.0007311287,0.0009415098,0.548309,0.04835952,0.05120854,0.02204553,0.3232664],"study_design_scores_gemma":[0.00003410035,0.00004266444,0.000187648,0.00001443041,0.00001536314,0.0001590127,0.00002179394,0.9704935,0.007128485,0.009728253,0.0121547,0.00001999695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004477404,0.00007011975,0.9826689,0.00008084345,0.00006056639,0.0001202896,0.0003327212,0.009686183,0.002503055],"genre_scores_gemma":[0.2366621,0.0002664536,0.7450212,0.0002685919,0.00006435279,0.0007617517,0.00280266,0.003304966,0.0108479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01057392,"threshold_uncertainty_score":0.03537327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08934396846312403,"score_gpt":0.302675087781499,"score_spread":0.213331119318375,"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."}}