{"id":"W1932653299","doi":"10.1109/scft.2000.878425","title":"Bandwidth extension of narrowband speech for low bit-rate wideband coding","year":2002,"lang":"en","type":"preprint","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Narrowband; Bandwidth extension; Wideband; Wideband audio; Bandwidth (computing); Computer science; Harmonic Vector Excitation Coding; Bit rate; Extension (predicate logic); Speech coding; Electronic engineering; Speech recognition; Telecommunications; Engineering; Computer network; Digital audio","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":[0.0001810514,0.0003485336,0.0002148836,0.0002914,0.0002033574,0.0002275785,0.0002306679,0.0003116223,0.002010854],"category_scores_gemma":[0.0008407075,0.0001056387,0.0001771531,0.0002048877,0.0002337076,0.0003568594,0.0003181906,0.0003716617,0.001076898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000134818,"about_ca_system_score_gemma":0.0001725303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005101721,"about_ca_topic_score_gemma":0.0008249588,"domain_scores_codex":[0.9998809,0.00002650355,0.000006663224,0.00001751714,0.00005562537,0.00001268246],"domain_scores_gemma":[0.9997756,0.0000979851,0.00001899453,0.00004567803,0.00005290766,0.000008840948],"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":[0.0003081496,0.00006494804,0.0006095136,0.0001674448,0.00001507974,0.0002519705,0.0002374733,0.06466103,0.3236271,0.03215495,0.001719188,0.5761832],"study_design_scores_gemma":[0.00002741476,0.0002132287,0.001201113,0.00007612024,0.00003419343,0.0007892798,0.00006516041,0.7033718,0.2441686,0.0214902,0.02852897,0.00003385878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04398252,0.0007007043,0.9511154,0.0000946533,0.00005028715,0.00003584967,0.00004281211,0.0007626811,0.003214916],"genre_scores_gemma":[0.4162989,0.001216365,0.5738925,0.0001044303,0.00007055981,0.0001209327,0.0004020647,0.0001764336,0.007717825],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002010854,"threshold_uncertainty_score":0.00672698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03865130738622296,"score_gpt":0.2960161395282367,"score_spread":0.2573648321420138,"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."}}