{"id":"W1985275536","doi":"10.1109/ism.2012.13","title":"High Capacity Logarithmic Audio Watermarking Based on the Human Auditory System","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Digital watermarking; Robustness (evolution); Computer science; Critical band; Logarithm; Speech recognition; Audio signal; Frequency domain; Distortion (music); Watermark; Algorithm; Artificial intelligence; Computer vision; Speech coding; Mathematics; Bandwidth (computing); Telecommunications","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":[0.0003179295,0.0003110503,0.0003116588,0.0005286447,0.0002670899,0.0005356902,0.0004800153,0.0003873575,0.001618519],"category_scores_gemma":[0.001277292,0.0001240285,0.0002075731,0.0004603572,0.0007480084,0.001654201,0.0006333488,0.0004015455,0.000533887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000246331,"about_ca_system_score_gemma":0.00018681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001773201,"about_ca_topic_score_gemma":0.0001707519,"domain_scores_codex":[0.9997361,0.00004731443,0.00001311213,0.00003867883,0.000145495,0.0000192243],"domain_scores_gemma":[0.9994986,0.0002254388,0.00008467761,0.00008214087,0.00008184716,0.00002724862],"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":[0.0006787685,0.00006478099,0.0005582913,0.000281566,0.00002397238,0.0003206719,0.00017335,0.03130507,0.6395879,0.02614441,0.0006439039,0.3002175],"study_design_scores_gemma":[0.00008806718,0.0008803028,0.001469954,0.00006617646,0.00005873369,0.001748743,0.00007295409,0.4918413,0.4734779,0.0111148,0.01904441,0.0001366292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08984526,0.001775468,0.9006476,0.0002654256,0.000121188,0.00006259503,0.00004243603,0.000907607,0.006332467],"genre_scores_gemma":[0.8085642,0.00109683,0.1855639,0.00007025543,0.0001173904,0.00003953297,0.00005209848,0.00005048604,0.004445328],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001618519,"threshold_uncertainty_score":0.005414426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02287745172535203,"score_gpt":0.2278759693864015,"score_spread":0.2049985176610495,"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."}}