{"id":"W4380995398","doi":"10.3390/app13127112","title":"Source Microphone Identification Using Swin Transformer","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"King Saud University","keywords":"Microphone; Computer science; Transformer; Digital audio; Identification (biology); Speech recognition; Audio analyzer; Artificial intelligence; Audio signal; Engineering; Telecommunications; Speech coding; Electrical engineering","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":[0.0005956467,0.00008821396,0.00008491656,0.0002447562,0.000296653,0.0003801787,0.0006790124,0.00003361249,0.000004326852],"category_scores_gemma":[0.00001899414,0.0000791556,0.0000332901,0.002526561,0.0002405402,0.0006148761,0.00005971322,0.00005564316,0.0006083765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002736289,"about_ca_system_score_gemma":0.00005935401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002090524,"about_ca_topic_score_gemma":0.000008914805,"domain_scores_codex":[0.9986622,0.00001084292,0.0001857925,0.0004076303,0.0004330096,0.0003005366],"domain_scores_gemma":[0.9995658,0.00005441012,0.00006335925,0.0002325935,0.00002399844,0.00005987307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001816967,0.00001459191,0.00005095378,0.000006570573,0.000003685853,0.000001216682,0.001093284,0.001329131,0.7672158,0.00975252,0.0002903974,0.22024],"study_design_scores_gemma":[0.000299535,0.00006460783,0.002487204,0.00001917828,0.000007244178,0.00002866424,0.0007235343,0.1103466,0.8539013,0.02558447,0.00611805,0.0004195591],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6031384,0.00001015659,0.390083,0.0002631225,0.0007489373,0.000141257,6.157311e-7,0.0003699573,0.005244508],"genre_scores_gemma":[0.9910562,0.000003000573,0.008495837,0.00009198656,0.00005191353,0.00001466658,0.000001162862,0.000005379943,0.0002798459],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3879178,"threshold_uncertainty_score":0.7819652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02935601443706471,"score_gpt":0.2557583507319746,"score_spread":0.2264023362949099,"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."}}