{"id":"W4312232211","doi":"10.1109/ijcnn55064.2022.9892702","title":"MRGAN: Multi-Criteria Relational GAN for Lyrics-Conditional Melody Generation","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Discriminator; Lyrics; Artificial intelligence; Generator (circuit theory); BLEU; Leverage (statistics); Speech recognition; Generative grammar; Key (lock); Metric (unit); Melody; Natural language processing; Machine learning; Musical; Machine translation; Power (physics)","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":[0.0008224341,0.001055741,0.0006176582,0.0003475467,0.0001832545,0.0004429086,0.001142006,0.0006095693,0.002688406],"category_scores_gemma":[0.001588137,0.0002414857,0.000519171,0.0003401174,0.0003844977,0.0006425657,0.0007651782,0.001083362,0.0005795648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005553706,"about_ca_system_score_gemma":0.0004884902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002085512,"about_ca_topic_score_gemma":0.003772158,"domain_scores_codex":[0.9996539,0.0001252063,0.00001252947,0.00009565828,0.00007638758,0.00003635273],"domain_scores_gemma":[0.9996277,0.0002067832,0.00002864609,0.00005823996,0.00005561473,0.00002295294],"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":[0.0001984852,0.000110519,0.001450805,0.0001309439,0.0001160035,0.0001813388,0.00006920316,0.7588423,0.01155624,0.01317249,0.008691199,0.2054805],"study_design_scores_gemma":[0.000008403066,0.00002334528,0.0001285122,0.000005031893,0.000008400741,0.0000321696,0.000004886697,0.9953669,0.001311455,0.002383498,0.000722696,0.000004712088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02933782,0.0009931333,0.9610521,0.0002729614,0.00009346584,0.0001007889,0.0003171295,0.001911918,0.00592074],"genre_scores_gemma":[0.7604572,0.0005367502,0.2270065,0.0005849532,0.00008176584,0.0002440383,0.001848405,0.0003628927,0.008877503],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002688406,"threshold_uncertainty_score":0.008993626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1070900726682258,"score_gpt":0.3022042917470935,"score_spread":0.1951142190788677,"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."}}