{"id":"W2716649223","doi":"10.1109/ccece.2017.7946647","title":"Speech enhancement using both spectral and spectral modulation domains","year":2017,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"PESQ; Estimator; Speech enhancement; Minimum mean square error; Modulation (music); Computer science; Speech recognition; Signal-to-noise ratio (imaging); Noise (video); Mathematics; SIGNAL (programming language); Algorithm; Statistics; Artificial intelligence; Acoustics; Physics; Noise reduction","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":[0.0004788255,0.0006591879,0.0004769264,0.0004351615,0.0001345649,0.0004633938,0.0003329764,0.0005123863,0.001051397],"category_scores_gemma":[0.001006116,0.0002003666,0.0004863799,0.0002304268,0.0003018593,0.0007405896,0.000530617,0.0004018009,0.000724155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009385332,"about_ca_system_score_gemma":0.0001580023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001848284,"about_ca_topic_score_gemma":0.0003570709,"domain_scores_codex":[0.9996979,0.00007465401,0.00001931865,0.00005077888,0.0001397874,0.00001752809],"domain_scores_gemma":[0.999582,0.0001835275,0.00004347104,0.00004549725,0.0001338322,0.00001163793],"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":[0.0004403069,0.00007802052,0.0007465389,0.0003031558,0.00008255602,0.0001425012,0.0001000264,0.01499523,0.6030869,0.003547869,0.0002600021,0.3762169],"study_design_scores_gemma":[0.00005906729,0.0009839827,0.00386322,0.0001067437,0.0002963115,0.001746379,0.00008344333,0.3016903,0.671854,0.003096518,0.01616013,0.00005981635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05117726,0.002012135,0.9421405,0.0001130431,0.00007211801,0.00005538108,0.00002250971,0.0004443132,0.003962787],"genre_scores_gemma":[0.506525,0.002289379,0.4832536,0.0001919604,0.0002508262,0.0000691603,0.0001123221,0.0001012911,0.007206398],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001051397,"threshold_uncertainty_score":0.00351727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02742055141142517,"score_gpt":0.2872277145663094,"score_spread":0.2598071631548842,"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."}}