{"id":"W2124510467","doi":"10.1186/1687-4722-2012-20","title":"Speech steganography using wavelet and Fourier transforms","year":2012,"lang":"en","type":"article","venue":"EURASIP Journal on Audio Speech and Music Processing","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Speech recognition; Steganography; Wavelet; SIGNAL (programming language); Information hiding; Cover (algebra); Discrete wavelet transform; Wavelet transform; Algorithm; Artificial intelligence; Embedding; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0008526702,0.0003178169,0.0003113858,0.0004736876,0.0007824098,0.0005852921,0.0003592084,0.0001144577,0.000005551996],"category_scores_gemma":[0.00002415819,0.0002402163,0.0001115986,0.0005838281,0.0001443899,0.002545377,0.00009484984,0.0005879943,0.000001170987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000323928,"about_ca_system_score_gemma":0.00005222365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001899467,"about_ca_topic_score_gemma":9.406884e-7,"domain_scores_codex":[0.998085,0.00007654203,0.0003794679,0.0003354686,0.0004167623,0.000706744],"domain_scores_gemma":[0.9989656,0.00004493053,0.0002514097,0.0002313998,0.00009559896,0.0004110104],"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":[0.00003796068,0.00008001079,0.003748978,0.00007477,0.00003103043,0.0001063562,0.001649912,0.000001514564,0.004726351,0.0009430685,0.0001187758,0.9884813],"study_design_scores_gemma":[0.01098804,0.003797577,0.08514317,0.00762364,0.0006027698,0.08738853,0.001742166,0.02302345,0.4345622,0.1847659,0.1515255,0.008836977],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5839328,0.002705764,0.4110683,0.0003685246,0.000374715,0.0001613204,0.000001603755,0.0001997119,0.00118727],"genre_scores_gemma":[0.7378605,0.0003334911,0.2606523,0.0007641295,0.0003398115,0.000002124528,4.770162e-7,0.00002193003,0.00002520791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9796443,"threshold_uncertainty_score":0.9795737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02785804578846332,"score_gpt":0.266802719991267,"score_spread":0.2389446742028037,"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."}}