{"id":"W2910489255","doi":"10.48550/arxiv.1901.04555","title":"Music Artist Classification with Convolutional Recurrent Neural Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Convolutional neural network; Spectrogram; Set (abstract data type); Bottleneck; Recurrent neural network; Feature (linguistics); Representation (politics); Feature extraction; Artificial intelligence; Speech recognition; Baseline (sea); Music information retrieval; Frame (networking); Pattern recognition (psychology); Artificial neural network; Art; Linguistics","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":[0.0009648174,0.001153686,0.0006695607,0.001093596,0.0003501464,0.0009223774,0.001170338,0.0007380287,0.001671744],"category_scores_gemma":[0.002257838,0.0003502629,0.0008281616,0.0009513806,0.0002686608,0.001135921,0.0006815383,0.001217526,0.001151562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008931251,"about_ca_system_score_gemma":0.0006035673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0127071,"about_ca_topic_score_gemma":0.02007686,"domain_scores_codex":[0.9995248,0.0000816224,0.00002672991,0.0001547261,0.0001137416,0.0000983623],"domain_scores_gemma":[0.9994338,0.000198365,0.00008072647,0.0001109335,0.0001401899,0.00003582806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008066855,0.000569976,0.007928584,0.0001614601,0.000296806,0.0002509138,0.0001303861,0.3235824,0.03403848,0.001962042,0.008838039,0.6214343],"study_design_scores_gemma":[0.000008797746,0.00004886064,0.0009824306,0.000006209748,0.00001777251,0.00001651362,0.00001404962,0.994359,0.003517765,0.0006591414,0.0003622187,0.00000729651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6664615,0.002436923,0.308851,0.000819164,0.0002922137,0.0001476112,0.001665573,0.008636096,0.01069],"genre_scores_gemma":[0.9246285,0.000370063,0.06505687,0.0001298138,0.00009309209,0.00005436937,0.002926273,0.0001164561,0.006624448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0127071,"threshold_uncertainty_score":0.02526629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09721697107236928,"score_gpt":0.1850776500403218,"score_spread":0.08786067896795248,"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."}}