{"id":"W4251023516","doi":"10.32920/ryerson.14644470","title":"An error resilient scheme of digital watermarking for MP3 streaming audio","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Digital watermarking; Computer science; Watermark; Network packet; Packet loss; Encryption; Audio over Ethernet; Authentication (law); Digital audio; Computer network; Real-time computing; Multimedia; Computer security; Audio signal; Computer hardware; Digital signal processing; 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":[0.0004637181,0.0004439121,0.0002947396,0.0004210696,0.0005964811,0.0004617264,0.0008435767,0.0008066111,0.002261002],"category_scores_gemma":[0.001182498,0.0001347598,0.0003786442,0.0003179298,0.0005135449,0.0007373334,0.0006053183,0.0006693221,0.0008734122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003806424,"about_ca_system_score_gemma":0.0004225814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002843485,"about_ca_topic_score_gemma":0.0003009283,"domain_scores_codex":[0.9996055,0.00006469521,0.00002711727,0.00009124923,0.0001738195,0.00003755501],"domain_scores_gemma":[0.9996159,0.00004504294,0.00006335961,0.0001433187,0.0001139824,0.00001820956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007482289,0.0001489862,0.0004862067,0.0003554288,0.0000786013,0.0008647269,0.0004060777,0.02677295,0.5258786,0.1429099,0.005131878,0.2962185],"study_design_scores_gemma":[0.0001971839,0.001132936,0.0009567002,0.0001076092,0.0001246348,0.002170221,0.00008808808,0.4184076,0.4829827,0.02346582,0.07022911,0.0001373049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07916109,0.001110527,0.9026676,0.0008612229,0.000748278,0.0004314088,0.000150558,0.001541191,0.01332809],"genre_scores_gemma":[0.6783504,0.0008511928,0.2963766,0.0003079798,0.0001993955,0.0002645063,0.0002078969,0.00006543757,0.02337653],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002261002,"threshold_uncertainty_score":0.007563829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02772391349038812,"score_gpt":0.2986569395313253,"score_spread":0.2709330260409372,"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."}}