{"id":"W2015926323","doi":"10.1016/s0167-6393(00)00081-9","title":"Speech enhancement using fourth-order cumulants and optimum filters in the subband domain","year":2002,"lang":"en","type":"article","venue":"Speech Communication","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Speech recognition; Speech enhancement; Computer science; Noise (video); Frequency domain; Gaussian noise; Noise reduction; Speech processing; Spectrogram; Linear predictive coding; Mathematics; Algorithm; Artificial intelligence","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":[0.0005361451,0.0006609115,0.0006466234,0.0006686425,0.0002879257,0.000714705,0.0003457527,0.0007272692,0.002358891],"category_scores_gemma":[0.001766455,0.0002765099,0.0007449473,0.0005075519,0.0004263121,0.001030369,0.0003951024,0.0006988305,0.0008343498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003793057,"about_ca_system_score_gemma":0.0004756684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007627142,"about_ca_topic_score_gemma":0.002055191,"domain_scores_codex":[0.9997001,0.00007429513,0.00001744699,0.00003913843,0.0001355997,0.00003353733],"domain_scores_gemma":[0.9992288,0.0004206312,0.0000712454,0.00009245674,0.0001611502,0.00002571263],"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":[0.001327113,0.0002116356,0.0009133206,0.0004144151,0.0001514569,0.0002031582,0.0001930488,0.07682367,0.3231341,0.04953032,0.003047785,0.5440501],"study_design_scores_gemma":[0.00005901329,0.000170924,0.00228001,0.00004606864,0.0001179406,0.0003942755,0.00003452926,0.8343133,0.1428057,0.01131793,0.008405898,0.00005442977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02356914,0.0006799663,0.9726846,0.0001443323,0.00007124922,0.00001940577,0.00003945013,0.0003083591,0.002483505],"genre_scores_gemma":[0.1681732,0.001224511,0.8230682,0.000109039,0.000158941,0.00004735813,0.0001592484,0.000184827,0.006874657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002358891,"threshold_uncertainty_score":0.007891238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0416666573999727,"score_gpt":0.2793254558020496,"score_spread":0.2376587984020769,"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."}}