{"id":"W2051795155","doi":"10.4018/ijmdem.2013100101","title":"Multimodal Information Fusion of Audiovisual Emotion Recognition Using Novel Information Theoretic Tools","year":2013,"lang":"en","type":"article","venue":"International Journal of Multimedia Data Engineering and Management","topic":"Music and Audio Processing","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Dimensionality reduction; Information fusion; Artificial intelligence; Entropy (arrow of time); Information theory; Mutual information; Pattern recognition (psychology); Feature vector; Curse of dimensionality; Feature (linguistics); Gaussian; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003786009,0.0000883889,0.0001070625,0.0003621993,0.00002658586,0.0002703053,0.0005685671,0.000033345,0.00001384735],"category_scores_gemma":[0.0001411436,0.0000789178,0.00002632959,0.00009657243,0.00001730278,0.008237656,0.0003679845,0.00008585625,0.000008108725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004265388,"about_ca_system_score_gemma":0.00001936629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002204479,"about_ca_topic_score_gemma":1.179234e-7,"domain_scores_codex":[0.9989105,0.00000957286,0.0004904896,0.00007388146,0.000422678,0.00009290332],"domain_scores_gemma":[0.9989634,0.00004722659,0.000413628,0.0001516729,0.0003757707,0.0000482566],"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":[0.00001347063,0.0000454609,0.00003791595,0.0001405706,0.00009724018,0.000001541552,0.0008232431,0.01013954,0.0021494,0.000819662,0.0002492992,0.9854826],"study_design_scores_gemma":[0.0008281098,0.00002891854,0.003474327,0.0002946294,0.00001947528,0.00004168891,0.0001183526,0.9926898,0.0005840909,0.0001107154,0.00171425,0.0000956506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0756832,0.00002051458,0.9230635,0.000249356,0.0007386918,0.000123131,0.00002548846,0.00001808778,0.00007802151],"genre_scores_gemma":[0.5873054,0.0001552497,0.4121883,0.000117336,0.00009774914,0.000002410727,0.0001289206,0.000003259402,0.000001333954],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.985387,"threshold_uncertainty_score":0.5972102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02663902934635845,"score_gpt":0.2496010653268889,"score_spread":0.2229620359805305,"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."}}