{"id":"W2066205956","doi":"10.1109/icassp.2013.6637597","title":"Exploiting structural relationships in audio music signals using Markov Logic Networks","year":2013,"lang":"en","type":"preprint","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Markov chain; Chord (peer-to-peer); Theoretical computer science; Logical analysis; Music theory; Artificial intelligence; Mathematics; Machine learning","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":[0.0009538584,0.0005484633,0.0005349462,0.001104631,0.0005476938,0.001753623,0.001351739,0.0008043296,0.001880005],"category_scores_gemma":[0.003747555,0.0005375707,0.0008521756,0.0008910495,0.001241548,0.003757155,0.001504258,0.001232541,0.0002762003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001179395,"about_ca_system_score_gemma":0.0009162242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005032643,"about_ca_topic_score_gemma":0.005844824,"domain_scores_codex":[0.9994548,0.0001674782,0.00002715751,0.0001571679,0.0001403997,0.00005299631],"domain_scores_gemma":[0.998596,0.0008939919,0.0002328328,0.0001317666,0.00008617782,0.00005920238],"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":[0.0002293911,0.00009696211,0.002284436,0.0001890455,0.0001292186,0.000375324,0.0003188622,0.5820622,0.0182796,0.2877595,0.001134139,0.1071414],"study_design_scores_gemma":[0.000007227264,0.00001430732,0.0001533517,0.000008532186,0.00001415738,0.00002091789,0.00001291879,0.8898515,0.0009735554,0.1084227,0.0005085454,0.00001239029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02233199,0.0001700242,0.9751498,0.0002853668,0.00001491245,0.00002086065,0.0001298083,0.0002386451,0.001658538],"genre_scores_gemma":[0.7161748,0.000598795,0.2800751,0.000233436,0.00007242974,0.0001039587,0.0005128753,0.0001031819,0.002125615],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005032643,"threshold_uncertainty_score":0.01000667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1171558734447032,"score_gpt":0.2859262872650188,"score_spread":0.1687704138203157,"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."}}