{"id":"W2102868031","doi":"10.1109/lsp.2005.851259","title":"Concurrent data transmission through analog speech channel using data hiding","year":2005,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Decoding methods; Data transmission; Speech coding; Information hiding; Bit error rate; Transmission (telecommunications); Speech recognition; Embedding; Voice activity detection; Channel (broadcasting); Set (abstract data type); SIGNAL (programming language); Speech processing; Computer hardware; Computer network; Artificial intelligence; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005678986,0.000307185,0.0002725813,0.0001622216,0.0004926869,0.0004009616,0.004422361,0.0000883691,0.00000387052],"category_scores_gemma":[0.000005834064,0.0002790913,0.00005292146,0.0005852538,0.0001391343,0.005652505,0.0006805797,0.0003639381,0.000003791695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005674076,"about_ca_system_score_gemma":0.00008337236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002204243,"about_ca_topic_score_gemma":0.00000160351,"domain_scores_codex":[0.9973046,0.00009253185,0.0004165019,0.00113546,0.0004866029,0.0005643261],"domain_scores_gemma":[0.9981005,0.00005374627,0.0002275169,0.001461392,0.00005260385,0.0001042252],"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":[0.000008827889,0.00007611819,0.00003141891,0.0001099642,0.00002472243,0.00006304475,0.0008282437,0.001845684,0.1091724,0.00005358185,0.003548547,0.8842375],"study_design_scores_gemma":[0.000308979,0.00002917437,0.000009082351,0.0005458456,0.00003478069,0.0001008911,0.00001438062,0.9086228,0.06538455,0.0008811885,0.02351873,0.0005495945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004564203,0.0008800659,0.9907985,0.002744151,0.0001885297,0.0001701819,0.00002816624,0.0005508586,0.0000753308],"genre_scores_gemma":[0.6347944,0.00003638035,0.3627559,0.001934592,0.0003833104,0.000002613508,0.00006680128,0.00002232263,0.00000364057],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9067771,"threshold_uncertainty_score":0.9999661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1171587703886166,"score_gpt":0.3357297328575816,"score_spread":0.2185709624689651,"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."}}