{"id":"W2532225645","doi":"10.1109/ispacs.2005.1595519","title":"Speech enhancement using adaptive neuro-fuzzy filtering","year":2005,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Adaptive neuro fuzzy inference system; Noise (video); Computer science; Recursive least squares filter; Fuzzy set; Filter (signal processing); Feature vector; Adaptive filter; Noise reduction; Feature (linguistics); Mathematics; Fuzzy control system; Pattern recognition (psychology); Fuzzy logic; Control theory (sociology); 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":[0.0003120753,0.0003031306,0.0003503188,0.0002759894,0.0002328175,0.0002996552,0.000402072,0.0004619772,0.0007841786],"category_scores_gemma":[0.0005659938,0.0001176661,0.0004131365,0.0001938106,0.0002142327,0.0003732056,0.0002376706,0.0003453948,0.0003297523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002205931,"about_ca_system_score_gemma":0.0001498501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001384243,"about_ca_topic_score_gemma":0.001842972,"domain_scores_codex":[0.9998518,0.00002689022,0.00001136042,0.00002819063,0.00007106317,0.00001065729],"domain_scores_gemma":[0.9998648,0.00005850732,0.00001183424,0.00001080165,0.00004982728,0.000004202739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003537764,0.0001177095,0.0007658873,0.0002700537,0.00009372301,0.0002970965,0.0002046513,0.175189,0.2311739,0.006135913,0.001113808,0.5842845],"study_design_scores_gemma":[0.00002179914,0.0001483575,0.0007961473,0.00002464281,0.00005227786,0.0001652464,0.00002214766,0.9410205,0.05040361,0.001958895,0.005363343,0.00002302106],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03146509,0.0006942334,0.9637665,0.00009324357,0.00009378821,0.00003469085,0.00001552205,0.0004751363,0.003361803],"genre_scores_gemma":[0.6167849,0.0008321511,0.376568,0.00009811851,0.00009694969,0.00005566578,0.00005668924,0.00004008006,0.005467538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001384243,"threshold_uncertainty_score":0.002752423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03513674312288446,"score_gpt":0.2671067142937065,"score_spread":0.231969971170822,"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."}}