{"id":"W4236926314","doi":"10.4018/9781599045139.ch004","title":"Robust Zero-Bit and Multi-Bit Audio Watermarking Using Correlation Detection and Chaotic Signals","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Digital watermarking; Watermark; Robustness (evolution); Chaotic; Computer science; Frequency domain; Audio signal; Algorithm; Speech recognition; SIGNAL (programming language); Artificial intelligence; Computer vision; Speech coding; Image (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":[0.0002440608,0.0005003974,0.0004039126,0.0008922508,0.0001709864,0.001012967,0.0005494399,0.0006982646,0.004959689],"category_scores_gemma":[0.0006117856,0.000265063,0.0003857555,0.0009369213,0.0006639067,0.001456217,0.0006504485,0.0006368303,0.002059669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002918861,"about_ca_system_score_gemma":0.0002946148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001818747,"about_ca_topic_score_gemma":0.0002331341,"domain_scores_codex":[0.9997532,0.00002720005,0.00001184108,0.00004671345,0.0001397045,0.00002148025],"domain_scores_gemma":[0.9997732,0.0001104339,0.00003141818,0.00004612759,0.00003167359,0.000007126596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000138453,0.00006393431,0.000207099,0.0007890664,0.00003205977,0.0004520993,0.0002244979,0.01652829,0.3139419,0.08105047,0.00320191,0.5833702],"study_design_scores_gemma":[0.00006080998,0.0005181507,0.001484437,0.0004192868,0.00009835563,0.003909848,0.0001704574,0.2742045,0.5544744,0.03876803,0.1257518,0.0001399644],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05353636,0.02144349,0.8620607,0.0004005843,0.000283892,0.0001406785,0.00009936914,0.001887016,0.06014803],"genre_scores_gemma":[0.4201775,0.02454704,0.4694166,0.0002266507,0.0002481949,0.0001148369,0.0003051533,0.0004072214,0.08455696],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004959689,"threshold_uncertainty_score":0.01659185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04509895800599724,"score_gpt":0.2405525911492233,"score_spread":0.195453633143226,"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."}}