{"id":"W2570693410","doi":"10.1109/icsai.2016.7811106","title":"An empirical study on mood classification in music through computational approaches","year":2016,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Feature selection; Computer science; Mood; Music information retrieval; Feature (linguistics); Empirical research; Machine learning; Artificial intelligence; Selection (genetic algorithm); Search engine indexing; Discretization; Statistical classification; Data mining; Psychology; Mathematics; Musical","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":[0.01022378,0.0004214598,0.0004439293,0.001815142,0.001020471,0.003019844,0.001201891,0.0008236647,0.003613748],"category_scores_gemma":[0.09873389,0.000256993,0.0006382661,0.003441524,0.001806685,0.003063143,0.001333773,0.001540071,0.0004797227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001084564,"about_ca_system_score_gemma":0.0007857804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002893686,"about_ca_topic_score_gemma":0.004172998,"domain_scores_codex":[0.991943,0.00546012,0.0003677371,0.0005908884,0.001428165,0.0002100231],"domain_scores_gemma":[0.8400277,0.1442663,0.005926011,0.004551513,0.00441959,0.0008088654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009219807,0.002331838,0.6977763,0.0009108544,0.0005168801,0.0001998847,0.005256861,0.01340766,0.001803486,0.02958052,0.005476376,0.2418173],"study_design_scores_gemma":[0.0001465273,0.0006981454,0.7500255,0.0005393535,0.0002331484,0.000578957,0.01064655,0.1910385,0.001352586,0.03349306,0.01115428,0.00009341304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9567951,0.001894947,0.02494799,0.002608756,0.00005588413,0.0001708587,0.000475931,0.00005214978,0.01299838],"genre_scores_gemma":[0.9853545,0.0004786247,0.01258132,0.0002730843,0.00005320782,0.000112645,0.0005273349,0.00001644842,0.0006026712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01022378,"threshold_uncertainty_score":0.05406922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2286889516539652,"score_gpt":0.3512274288754052,"score_spread":0.12253847722144,"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."}}