{"id":"W4404037259","doi":"10.1109/mlsp58920.2024.10734782","title":"Comparison of Hand-Crafted and Deep Features Towards Explainable AI at the Edge for Analysis of Audio Scenes","year":2024,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Computer vision; Computer graphics (images); Speech recognition","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":[0.0006732293,0.0006235021,0.0004274615,0.0005640389,0.0002158189,0.0009139394,0.001158625,0.0005682898,0.002796144],"category_scores_gemma":[0.003387464,0.0002448934,0.0005208419,0.0005691653,0.0004801826,0.002130684,0.0008042364,0.001227456,0.0006866489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006554889,"about_ca_system_score_gemma":0.0004857219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002808948,"about_ca_topic_score_gemma":0.003839763,"domain_scores_codex":[0.9997012,0.00006545185,0.00001317461,0.00008939479,0.00009420043,0.0000365273],"domain_scores_gemma":[0.9984114,0.0009304282,0.0001009975,0.0003499698,0.0001599768,0.00004710492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005966212,0.0001767217,0.002767383,0.0001854438,0.0001032707,0.0001399544,0.0001448905,0.2417571,0.04420007,0.01061601,0.003065869,0.6962466],"study_design_scores_gemma":[0.000008914432,0.0000688897,0.0008083131,0.000007636059,0.00001301616,0.00003013511,0.00001780924,0.9811523,0.01281767,0.004009883,0.001057367,0.000008076985],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1782851,0.0008057583,0.8077778,0.0003467915,0.00005021645,0.00006863736,0.0003389226,0.007357467,0.004969251],"genre_scores_gemma":[0.6545215,0.0003362214,0.3400394,0.0001306925,0.00003074308,0.00006010203,0.001183378,0.0004959183,0.003202071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002808948,"threshold_uncertainty_score":0.009354055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03307250634389632,"score_gpt":0.3389516788015922,"score_spread":0.3058791724576959,"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."}}