{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001073859,0.0002376815,0.0001711472,0.000236114,0.00009282699,0.0002201271,0.0002310015,0.000233187,0.0008544002],"category_scores_gemma":[0.0002385931,0.0001033971,0.0002588777,0.000246039,0.0001746069,0.0004576779,0.0002557093,0.0002158901,0.0002934165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001287063,"about_ca_system_score_gemma":0.0001350027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004557295,"about_ca_topic_score_gemma":0.0005486207,"domain_scores_codex":[0.9999193,0.00000790469,0.00000437884,0.00001472761,0.00004504664,0.000008636195],"domain_scores_gemma":[0.9998993,0.00003004905,0.00001748385,0.00001702878,0.00003130328,0.000004782418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002802944,0.00006685749,0.0005670927,0.0001234891,0.00003068336,0.0001770604,0.00009815819,0.02311059,0.8111668,0.002576517,0.0003885533,0.1614139],"study_design_scores_gemma":[0.00002048594,0.0002669004,0.0009449919,0.00001089871,0.00003419852,0.0006545041,0.0000303152,0.3382535,0.6552002,0.0005633067,0.004000855,0.00001996571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3332166,0.0007468667,0.6618094,0.0001069265,0.00006753871,0.00004092755,0.00008328535,0.0007840102,0.003144401],"genre_scores_gemma":[0.8783209,0.0005155919,0.1150324,0.00003487161,0.00002125295,0.00002241958,0.0001083953,0.00003612873,0.00590798],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008544002,"threshold_uncertainty_score":0.002858281,"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."}}