{"id":"W2168133875","doi":"10.1109/iscas.2005.1465296","title":"Artificial Bandwidth Extension of Telephony Speech by Data Hiding","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Bandwidth extension; Telephony; Computer science; Extension (predicate logic); Bandwidth (computing); Speech recognition; Telecommunications; Speech coding; Programming language","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":[0.0001497466,0.0001876562,0.0001514521,0.0002160915,0.00009311831,0.0001195166,0.0002317531,0.0002136827,0.0004409958],"category_scores_gemma":[0.0004945629,0.0001018207,0.0001553865,0.0001660709,0.0002816656,0.0004538874,0.0003747197,0.000210799,0.0001586439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009705005,"about_ca_system_score_gemma":0.00006869825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001076672,"about_ca_topic_score_gemma":0.0001216604,"domain_scores_codex":[0.9999014,0.0000230501,0.000007684087,0.00001431343,0.00004135116,0.00001216442],"domain_scores_gemma":[0.9997557,0.00009646893,0.00004243344,0.0000618387,0.00003460279,0.000009024904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003480458,0.00005648121,0.0004715077,0.000153241,0.00001547849,0.0001475022,0.0001753581,0.0147218,0.842063,0.004257256,0.0002563533,0.137334],"study_design_scores_gemma":[0.00002636301,0.0002213473,0.001094775,0.00002071845,0.00003022813,0.0007655606,0.00003101215,0.2158017,0.7749621,0.001651395,0.005368148,0.00002663155],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5705926,0.0007559803,0.4258055,0.0001127754,0.00005334965,0.00002424706,0.00003011558,0.0005036501,0.002121788],"genre_scores_gemma":[0.9476672,0.0003157586,0.05056717,0.00002254954,0.00001909922,0.00001428105,0.00003754776,0.00002202842,0.001334363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004409958,"threshold_uncertainty_score":0.001475215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05066735251417853,"score_gpt":0.3174690984815497,"score_spread":0.2668017459673712,"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."}}