{"id":"W4288070505","doi":"10.18280/ts.390324","title":"Hybrid Transform Based Speech Band Width Enhancement Using Data Hiding","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Speech recognition; Telephony; Wideband audio; Speech coding; Bandwidth (computing); Wideband; Voice activity detection; Bandwidth extension; Discrete cosine transform; Telephone network; Speech enhancement; Speech processing; Electronic engineering; Telecommunications; Artificial intelligence; Engineering; Digital audio; Background noise; Audio signal","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008777385,0.0002347574,0.0002264686,0.0001818653,0.0005669362,0.0001402772,0.002999137,0.00001730241,0.001581177],"category_scores_gemma":[0.000008431901,0.0002405087,0.00005859249,0.0003008269,0.00003727172,0.001191815,0.001107881,0.0002442354,0.000006158996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002340009,"about_ca_system_score_gemma":0.0001483042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002975614,"about_ca_topic_score_gemma":0.000001946283,"domain_scores_codex":[0.9972076,0.0001355624,0.00045397,0.0008123804,0.0009476886,0.0004427795],"domain_scores_gemma":[0.9982864,0.00008847294,0.0001639463,0.001305058,0.00003872092,0.000117381],"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":[0.0001727361,0.001298015,0.0001571639,0.0001069161,0.00009817054,0.0003878066,0.0004385094,0.009535589,0.3355072,0.005118398,0.02641885,0.6207606],"study_design_scores_gemma":[0.0007411083,0.0002252135,0.00001684179,0.000037982,0.00001637935,0.00003554908,0.00001679939,0.5615535,0.3776628,0.002364507,0.05697469,0.0003546953],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007173094,0.00008774485,0.9905083,0.0004971568,0.0002112165,0.0005318436,0.0002115531,0.0003348843,0.0004441609],"genre_scores_gemma":[0.658946,0.000007337105,0.3398708,0.0006939507,0.00006588647,0.00007460161,0.0002881206,0.00002002123,0.00003327464],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.651773,"threshold_uncertainty_score":0.9993315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05924471149776857,"score_gpt":0.2979697449892268,"score_spread":0.2387250334914582,"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."}}