{"id":"W2726605708","doi":"10.4324/9781315621364.ch38","title":"Dynamic Bayesian Networks for Musical Interaction","year":2017,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Music Technology and Sound Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Embodied cognition; Dynamic Bayesian network; Computer science; Formalism (music); Probabilistic logic; Set (abstract data type); Bayesian probability; Bayesian network; Musical; Cognitive science; Artificial intelligence; Psychology","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":[0.004916441,0.001590139,0.004085587,0.003596496,0.001789737,0.004383392,0.004124881,0.005232564,0.01386554],"category_scores_gemma":[0.03640844,0.002351285,0.002270444,0.003780956,0.003970176,0.006770748,0.003367758,0.006158658,0.001781358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005816553,"about_ca_system_score_gemma":0.002051644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03236572,"about_ca_topic_score_gemma":0.02120754,"domain_scores_codex":[0.9971594,0.001354594,0.000108776,0.0007215545,0.0004341975,0.0002215231],"domain_scores_gemma":[0.9724624,0.02349841,0.001538948,0.0006542811,0.001102626,0.0007434346],"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":[0.00009438687,0.00006651207,0.0006234898,0.0001451691,0.0001054731,0.00007934539,0.0001797743,0.5999928,0.000212253,0.3724484,0.004105809,0.02194677],"study_design_scores_gemma":[0.00001619353,0.000007075766,0.0001723677,0.0000215226,0.00001727212,0.00001436286,0.00001469502,0.7387717,0.0000347401,0.2595696,0.001340921,0.00001947326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.014202,0.002402642,0.9715978,0.002542315,0.000162124,0.00008132042,0.0005900654,0.0003416509,0.008080089],"genre_scores_gemma":[0.6833307,0.008941029,0.2347282,0.001035494,0.001627349,0.001098456,0.002738644,0.0006663131,0.06583384],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03236572,"threshold_uncertainty_score":0.06435466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0131331746061885,"score_gpt":0.2496682257013067,"score_spread":0.2365350510951182,"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."}}