{"id":"W2956699086","doi":"10.48550/arxiv.1907.04928","title":"Bag-of-Audio-Words based on Autoencoder Codebook for Continuous Emotion Prediction","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Codebook; Autoencoder; Pattern recognition (psychology); Computer science; Artificial intelligence; Metric (unit); Speech recognition; Dimension (graph theory); Concordance correlation coefficient; Histogram; Artificial neural network; Mathematics; Statistics; Image (mathematics)","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":[0.0006571133,0.0008629806,0.0008673358,0.000871732,0.0001887726,0.0005838792,0.0009943263,0.0005805915,0.002431253],"category_scores_gemma":[0.002495183,0.0002787434,0.0005456908,0.0009892776,0.0003010193,0.001451798,0.0007863812,0.001220113,0.001811915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004567881,"about_ca_system_score_gemma":0.0005267627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004326774,"about_ca_topic_score_gemma":0.004547327,"domain_scores_codex":[0.9993285,0.0001178069,0.00003889591,0.0002262231,0.000219965,0.00006867242],"domain_scores_gemma":[0.9991909,0.0003057815,0.00005984573,0.0001070982,0.0003019232,0.00003452679],"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":[0.0003826076,0.000177791,0.001350987,0.0001732255,0.00009905002,0.0001005584,0.0001042481,0.06849605,0.03938207,0.003990989,0.006966475,0.8787758],"study_design_scores_gemma":[0.00001932803,0.0001094188,0.001378785,0.00002415348,0.0000287798,0.00008037749,0.0000412011,0.9817629,0.01058866,0.003528834,0.002415084,0.00002243507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02338753,0.001298509,0.971221,0.0001185506,0.0002089808,0.00007085033,0.0003599332,0.002095699,0.001238915],"genre_scores_gemma":[0.4892121,0.001402818,0.4959055,0.0003007211,0.0002396247,0.0002896778,0.003469472,0.0003498354,0.008830258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004326774,"threshold_uncertainty_score":0.008603156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05484333544818904,"score_gpt":0.187602036019208,"score_spread":0.132758700571019,"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."}}