{"id":"W4307743400","doi":"10.32920/21428682","title":"Audio Signal Processing Using Time-Frequency Approaches: Coding, Classification, Fingerprinting, and Watermarking","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Digital watermarking; Audio signal; Digital audio; Speech recognition; Speech coding; Audio signal processing; Psychoacoustics; Coding (social sciences); Artificial intelligence; Perception; Mathematics","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":[0.0005745093,0.0007090404,0.0004701208,0.001788391,0.0003145576,0.0014896,0.0005924589,0.001517784,0.0021831],"category_scores_gemma":[0.001578811,0.0002274639,0.0004120486,0.002530544,0.001258177,0.001821017,0.0005958785,0.0009010714,0.001237985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004154803,"about_ca_system_score_gemma":0.0003245519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001435983,"about_ca_topic_score_gemma":0.001085775,"domain_scores_codex":[0.9994771,0.00008673872,0.00002816956,0.00009678434,0.0002820495,0.00002925488],"domain_scores_gemma":[0.9994659,0.0002033294,0.00006720932,0.00008709393,0.0001549975,0.00002150365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001208435,0.00007410502,0.0004855509,0.0004150635,0.00004176561,0.0001527335,0.0001424991,0.01442489,0.07954502,0.04809884,0.003727284,0.8527714],"study_design_scores_gemma":[0.00004110963,0.0005823583,0.004243589,0.0003820106,0.0001314867,0.002550466,0.0003265909,0.6094732,0.1395013,0.1369746,0.1056105,0.0001827948],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007937537,0.01753964,0.9654526,0.0005844743,0.0004298044,0.0000565984,0.0000519448,0.0003845162,0.00756301],"genre_scores_gemma":[0.2207747,0.0452657,0.7047077,0.0006327153,0.002301803,0.0001455032,0.000309541,0.0001616908,0.02570062],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0021831,"threshold_uncertainty_score":0.007303238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0966879786237196,"score_gpt":0.2722731349881855,"score_spread":0.1755851563644659,"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."}}