{"id":"W7023630230","doi":"","title":"Optical music recognition of square notation using generative adversarial networks","year":2021,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Interdisciplinary Research in Music Media and Technology","funders":"","keywords":"Workflow; Notation; Generative grammar; Set (abstract data type); Deep learning; Artificial neural network; Musical notation; Training set; Adversarial system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0009351964,0.0009276801,0.0005323447,0.0003569557,0.0001976032,0.0008902921,0.001087707,0.0008079673,0.001726177],"category_scores_gemma":[0.002825712,0.0003226096,0.0008245246,0.0002518799,0.0007278059,0.0005982441,0.0007776172,0.001467076,0.0006524732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00073081,"about_ca_system_score_gemma":0.0004995227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003417809,"about_ca_topic_score_gemma":0.003226262,"domain_scores_codex":[0.9994653,0.0001629569,0.00001917586,0.0001523078,0.0001298683,0.00007035308],"domain_scores_gemma":[0.9988525,0.0006779097,0.000111928,0.0001576795,0.0001490783,0.00005086073],"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":[0.0001741497,0.00006639754,0.0008040094,0.0000510798,0.00004195948,0.0001445757,0.00003889131,0.8925366,0.009023829,0.00289097,0.002040393,0.09218706],"study_design_scores_gemma":[0.000002930632,0.00001614195,0.00008737848,0.000002903195,0.000002442419,0.00001564282,0.000003668723,0.9972041,0.001847706,0.0006173846,0.0001963533,0.000003420696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1614215,0.0009751579,0.8228207,0.0008400705,0.0003223556,0.0001686889,0.0003061522,0.003766572,0.009378856],"genre_scores_gemma":[0.8889741,0.0002628264,0.1019228,0.000395002,0.00007229202,0.00008706463,0.0005744792,0.0001540648,0.007557371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003417809,"threshold_uncertainty_score":0.006795824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03722655519944967,"score_gpt":0.2555813577815327,"score_spread":0.2183548025820831,"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."}}