{"id":"W4395032600","doi":"10.31234/osf.io/yptsr","title":"Measuring self-similarity in empirical signals to understand musical beat perception","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Laboratory for Brain, Music and Sound Research","funders":"Fonds De La Recherche Scientifique - FNRS; Danmarks Grundforskningsfond; National Research Foundation","keywords":"Beat (acoustics); Musical; Perception; Similarity (geometry); Psychology; Empirical research; Speech recognition; Cognitive psychology; Computer science; Communication; Artificial intelligence; Mathematics; Art; Acoustics; Statistics; Physics; Neuroscience; Visual arts","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008234208,0.0003282067,0.0004118429,0.0003566348,0.00009457102,0.0008952045,0.0009453325,0.0003531136,0.0001045364],"category_scores_gemma":[0.00004686415,0.0002915339,0.000147387,0.0005194045,0.00002913074,0.000175647,0.00378705,0.001127592,0.0001706526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005573562,"about_ca_system_score_gemma":0.0004249718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006657252,"about_ca_topic_score_gemma":0.0001203412,"domain_scores_codex":[0.9971242,0.000118279,0.0004319551,0.001266051,0.000624883,0.0004346216],"domain_scores_gemma":[0.9989536,0.00007647112,0.00005695197,0.0006167972,0.00007825392,0.000217938],"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":[0.0001171954,0.002430719,0.007057182,0.009335323,0.0005542387,0.001669146,0.289731,0.0381163,0.01154103,0.03476833,0.2305846,0.374095],"study_design_scores_gemma":[0.0008072468,0.000196681,0.01859717,0.003369339,0.0001149686,0.00007138125,0.001675984,0.6399838,0.001562014,0.3248387,0.005691501,0.003091293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.209065,0.0001280465,0.7583548,0.01654262,0.0008928296,0.0003935267,0.000002736396,0.0007069571,0.01391351],"genre_scores_gemma":[0.915588,0.00001121719,0.07961169,0.004179179,0.0002958467,0.00001901407,0.000002702065,0.0000212999,0.0002710841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.706523,"threshold_uncertainty_score":0.9999537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1363317198727109,"score_gpt":0.3286214100538628,"score_spread":0.1922896901811519,"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."}}