{"id":"W2987035182","doi":"","title":"Analysis of Polyphonic Musical Time Series.","year":2008,"lang":"en","type":"article","venue":"GfKl","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Polyphony; Computer science; Speech recognition; Preprocessor; Musical; Series (stratigraphy); Artificial intelligence; Acoustics; Art","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009415685,0.000971347,0.0007949273,0.001408023,0.0002840252,0.001181542,0.001183615,0.001113081,0.004666365],"category_scores_gemma":[0.00331706,0.0002773146,0.001011572,0.001410002,0.0005885561,0.001361412,0.0008336031,0.001067291,0.002233143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003213017,"about_ca_system_score_gemma":0.0004398907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001088533,"about_ca_topic_score_gemma":0.0008828452,"domain_scores_codex":[0.9993381,0.0001102321,0.00004686608,0.0001876255,0.0002699669,0.000047246],"domain_scores_gemma":[0.9993857,0.0002794311,0.0001060583,0.0001097599,0.00009165211,0.00002742163],"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":[0.0001967516,0.0001560808,0.003914691,0.001760607,0.0003435135,0.0009244549,0.0004709843,0.168687,0.1228896,0.1582644,0.01249648,0.5298954],"study_design_scores_gemma":[0.00001323627,0.0001485873,0.006751143,0.00009425818,0.00008231763,0.001060214,0.00008161862,0.8522635,0.01139334,0.08856481,0.03948454,0.00006254004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003399276,0.001496621,0.9927679,0.000134132,0.000108933,0.00005442928,0.0003399148,0.000308812,0.001389973],"genre_scores_gemma":[0.2351259,0.008392683,0.733898,0.0004326499,0.0006650731,0.000500081,0.003413219,0.0003550869,0.01721718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004666365,"threshold_uncertainty_score":0.01561052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01314136577504492,"score_gpt":0.2166111998574823,"score_spread":0.2034698340824374,"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."}}