{"id":"W2012792043","doi":"10.1109/wcins.2010.5541869","title":"Wideband audio over narrowband based on digital watermarking","year":2010,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Narrowband; Digital watermarking; Computer science; Wideband audio; Wideband; Audio signal; Bandwidth (computing); Digital audio; Sound quality; Scheme (mathematics); Audio signal flow; Audio signal processing; SIGNAL (programming language); Signal processing; Digital signal processing; Electronic engineering; Computer hardware; Speech recognition; Telecommunications; Engineering; Artificial intelligence; 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":[0.0002186097,0.0003226794,0.0002787598,0.0005059711,0.0002756426,0.0004976938,0.0003194621,0.0003998936,0.002595175],"category_scores_gemma":[0.0007761284,0.0001377736,0.0001993558,0.0003342741,0.0003215981,0.001018502,0.000672772,0.0003523677,0.0007676419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001671721,"about_ca_system_score_gemma":0.0001386903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001703452,"about_ca_topic_score_gemma":0.0002564752,"domain_scores_codex":[0.9997317,0.00004245663,0.00001943268,0.00005096768,0.0001290962,0.00002632027],"domain_scores_gemma":[0.999738,0.00007532338,0.00003997535,0.00006292659,0.00006414732,0.0000196115],"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":[0.0004806547,0.00005574961,0.0004468008,0.0002724667,0.00002069285,0.0003315909,0.0001369781,0.006158371,0.6870497,0.02300731,0.0006861297,0.2813535],"study_design_scores_gemma":[0.0001342903,0.001277529,0.001570349,0.0001975999,0.0002250555,0.002000368,0.0001115635,0.1139239,0.7592739,0.01664,0.1045572,0.00008823006],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1552429,0.003089165,0.8120056,0.0003142436,0.0004069253,0.0001428871,0.00007163586,0.001550604,0.027176],"genre_scores_gemma":[0.7439101,0.00301601,0.2322919,0.0001946601,0.000356073,0.00008430641,0.00008970743,0.00009848207,0.01995876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002595175,"threshold_uncertainty_score":0.008681774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005640979982272209,"score_gpt":0.2149543370434132,"score_spread":0.209313357061141,"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."}}