{"id":"W2045626557","doi":"10.1109/ccece.2010.5575108","title":"A robust digital audio watermarking algorithm using Empirical Mode Decomposition","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Digital watermarking; Hilbert–Huang transform; Computer science; Robustness (evolution); Algorithm; Reverberation; Speech recognition; Signal processing; Audio signal processing; Audio signal; Frequency domain; Discrete cosine transform; Quantization (signal processing); Digital signal processing; Artificial intelligence; Speech coding; White noise; Computer vision; Acoustics; Telecommunications; Computer hardware","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":[0.0005764301,0.0004505386,0.0004620183,0.0007296132,0.0001984003,0.0003990341,0.0004787514,0.0006016837,0.001208614],"category_scores_gemma":[0.00109889,0.0002132622,0.0004971357,0.0004800814,0.0003411423,0.001042304,0.0004988522,0.0007566405,0.0006159536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000216264,"about_ca_system_score_gemma":0.0002713299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003713252,"about_ca_topic_score_gemma":0.0003489343,"domain_scores_codex":[0.9996287,0.00005975866,0.00002632182,0.0000805536,0.0001828985,0.00002180498],"domain_scores_gemma":[0.9996698,0.00009524261,0.00004358952,0.00005560317,0.0001211439,0.00001460375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002233313,0.00005776739,0.0006305229,0.0001634642,0.0000685864,0.0001075557,0.00008889734,0.03271971,0.211835,0.01214172,0.001174721,0.7407887],"study_design_scores_gemma":[0.00005656682,0.0002488226,0.001128591,0.00002670537,0.00004453836,0.0006005126,0.00003217813,0.8881416,0.09394369,0.003690128,0.01203154,0.000055075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008603807,0.0003537258,0.9900747,0.0000632416,0.00006251835,0.00002162096,0.00001412099,0.0003165532,0.0004897223],"genre_scores_gemma":[0.1108647,0.000442818,0.8857409,0.00005812553,0.00007524156,0.00005069945,0.0001095513,0.00005491434,0.002602895],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001208614,"threshold_uncertainty_score":0.004043162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03101613005916845,"score_gpt":0.3367826254914182,"score_spread":0.3057664954322498,"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."}}