{"id":"W4399812941","doi":"10.58532/v3bfit2p8ch2","title":"ASA: AUDIO SENTIMENT ANALYSIS AFTER A SINGLE-CHANNEL MULTIPLE SOURCE SEPARATION","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Mixture model; Speech recognition; Cluster analysis; Speaker diarisation; Segmentation; Artificial intelligence; Speaker recognition; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002256272,0.0004062007,0.0005134239,0.0005691346,0.0001600327,0.0004401739,0.0005210149,0.0002642376,0.0003377876],"category_scores_gemma":[0.00001380425,0.0003706042,0.0004499045,0.0003383872,0.00004761064,0.000325085,0.0004965137,0.000228701,0.001073796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001054773,"about_ca_system_score_gemma":0.00006643748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000021826,"about_ca_topic_score_gemma":0.0001383147,"domain_scores_codex":[0.9976503,0.000019139,0.0004582582,0.0009181657,0.0006044019,0.0003496979],"domain_scores_gemma":[0.9985886,0.00008984638,0.0003019682,0.0007471479,0.0001410357,0.000131417],"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":[0.000255935,0.0009787016,0.00147381,0.001480044,0.02430181,0.001634067,0.02483755,0.05405271,0.002050847,0.1428497,0.2705231,0.4755618],"study_design_scores_gemma":[0.0009603339,0.0001897491,0.0005366224,0.0005552432,0.00211502,0.00002389456,0.00004914051,0.6661542,0.001383977,0.02186892,0.3030394,0.003123526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00004732693,0.0001332995,0.8312269,0.0005717746,0.000339975,0.0001744494,0.000006834512,0.0005190665,0.1669804],"genre_scores_gemma":[0.03863643,0.00001281976,0.007159771,0.001187334,0.0002685506,0.00003007766,0.00004267991,0.00005164385,0.9526107],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8240671,"threshold_uncertainty_score":0.9998746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03346154438190561,"score_gpt":0.2453551173682862,"score_spread":0.2118935729863806,"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."}}