{"id":"W2786498952","doi":"10.1109/acii.2017.8273600","title":"Emo-soundscapes: A dataset for soundscape emotion recognition","year":2017,"lang":"en","type":"article","venue":"","topic":"Noise Effects and Management","field":"Health Professions","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Soundscape; Crowdsourcing; Computer science; Speech recognition; Arousal; Active listening; Natural language processing; Valence (chemistry); Classifier (UML); Artificial intelligence; Psychology; Sound (geography); Communication; World Wide Web; Acoustics","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":[0.0006841495,0.002329813,0.0008870668,0.00302452,0.0007803507,0.001116781,0.001354224,0.001907677,0.00868861],"category_scores_gemma":[0.003042122,0.0003272569,0.001093484,0.002123149,0.0003936111,0.0009203675,0.00203189,0.001051706,0.01302044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007026825,"about_ca_system_score_gemma":0.000719949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009506075,"about_ca_topic_score_gemma":0.0232615,"domain_scores_codex":[0.9987624,0.0002437933,0.0001911225,0.0002844899,0.0003798562,0.0001385201],"domain_scores_gemma":[0.998597,0.000378642,0.0001366715,0.0002624559,0.000415161,0.0002099948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001288786,0.0004614751,0.01360942,0.004037554,0.0002813984,0.001064326,0.00094702,0.002116972,0.02069131,0.001273849,0.8594816,0.09474628],"study_design_scores_gemma":[0.0004401267,0.0005047141,0.1418183,0.0006011214,0.0002299672,0.002059747,0.001885899,0.01373981,0.01922403,0.002658154,0.8164558,0.0003820968],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04036527,0.00148167,0.008611267,0.0004443792,0.0006449553,0.0006805176,0.9278669,0.00910441,0.01080068],"genre_scores_gemma":[0.03214892,0.0004091971,0.01164973,0.0002596985,0.0001446122,0.001419091,0.9483216,0.0004548178,0.005192437],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009506075,"threshold_uncertainty_score":0.02906626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.159484154246398,"score_gpt":0.4764967253364276,"score_spread":0.3170125710900296,"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."}}