{"id":"W4392905907","doi":"10.32920/25413826.v1","title":"Super-resolution of Audio Files Using Feed-forward Neural Networks for Music Storage and Transfer","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Encoder; Audio signal; Transfer (computing); SIGNAL (programming language); Speech recognition; Audio signal flow; Lossy compression; Matching (statistics); Digital audio; Computer hardware; Real-time computing; Speech coding; Artificial intelligence","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":[0.0003401617,0.0002959898,0.000270241,0.0002538486,0.000226999,0.0004322537,0.0005477614,0.0004315764,0.002254153],"category_scores_gemma":[0.0009762964,0.000177768,0.000215844,0.0003632898,0.0002479864,0.0009198055,0.000353091,0.00063985,0.0005161788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004661148,"about_ca_system_score_gemma":0.0003709651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002838361,"about_ca_topic_score_gemma":0.006156314,"domain_scores_codex":[0.9998535,0.0000203556,0.00000838554,0.00002872978,0.00007176183,0.00001731031],"domain_scores_gemma":[0.9997154,0.0001446493,0.0000231611,0.00003289306,0.00007437568,0.000009596372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003849964,0.00009913529,0.0005654307,0.0001848615,0.0000533532,0.0002465157,0.0001199386,0.1181083,0.2261511,0.007195679,0.002294428,0.6445963],"study_design_scores_gemma":[0.000008646835,0.00005541413,0.0003857413,0.00001278885,0.00001777128,0.00009566079,0.00002316857,0.9289066,0.06540507,0.001894927,0.003180065,0.00001416321],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04288533,0.001137,0.9519601,0.0002210937,0.00008614433,0.00003673992,0.00008625424,0.001162385,0.002424982],"genre_scores_gemma":[0.4485764,0.001071576,0.5389514,0.0001317942,0.00008080932,0.00006470253,0.0002376534,0.0001070735,0.01077854],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002838361,"threshold_uncertainty_score":0.007540882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03627816833476384,"score_gpt":0.2895799423281203,"score_spread":0.2533017739933565,"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."}}