{"id":"W1998687781","doi":"10.1109/ccece.2010.5575180","title":"Bandwidth extension for speech enhancement","year":2010,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Bandwidth extension; Narrowband; Wideband audio; Bandwidth (computing); Computer science; Speech enhancement; Speech recognition; Wideband; Intelligibility (philosophy); Voice activity detection; Speech processing; Speech coding; Noise reduction; Electronic engineering; Telecommunications; Artificial intelligence; Audio signal; Engineering; 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":[0.0003850101,0.0004503216,0.0002917017,0.0003104299,0.0001772983,0.0002934025,0.0003548177,0.0004821286,0.001600813],"category_scores_gemma":[0.0009454848,0.0001512322,0.000269362,0.0002160071,0.0004370687,0.0005927244,0.000650637,0.0005112477,0.0005942242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001031054,"about_ca_system_score_gemma":0.0001076204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002145962,"about_ca_topic_score_gemma":0.0002372221,"domain_scores_codex":[0.9998399,0.00005253336,0.00001007105,0.00003757231,0.00004565471,0.0000143524],"domain_scores_gemma":[0.9996296,0.0001968268,0.00003436357,0.00006646189,0.00005889837,0.00001383671],"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":[0.0005250353,0.00007886619,0.0003951995,0.0003574326,0.00003718957,0.0002645621,0.0002392222,0.03885975,0.5256659,0.02110649,0.000698003,0.4117723],"study_design_scores_gemma":[0.00007383516,0.0007265666,0.003180921,0.0002482278,0.0001610303,0.003069281,0.0001551783,0.5826089,0.3301136,0.03093136,0.04860987,0.0001212902],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0406056,0.002887744,0.951953,0.00009094467,0.00006390816,0.00005619098,0.00002284323,0.0004130069,0.003906784],"genre_scores_gemma":[0.6010928,0.003465552,0.3897294,0.000139518,0.0001510014,0.000118492,0.00008685651,0.00010414,0.005112141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001600813,"threshold_uncertainty_score":0.005355239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01379990371383165,"score_gpt":0.2661561404996358,"score_spread":0.2523562367858042,"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."}}