{"id":"W2052628454","doi":"10.1109/97.855449","title":"GMDF for noise reduction and echo cancellation","year":2000,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Echo (communications protocol); Noise reduction; Reduction (mathematics); Microphone; Noise (video); Frequency domain; Telephony; Algorithm; Speech enhancement; Computer science; Computation; Block (permutation group theory); Signal-to-noise ratio (imaging); Return loss; Speech recognition; Telecommunications; Mathematics; Artificial intelligence; Antenna (radio); Computer network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000164687,0.0001318991,0.0001150784,0.00007678552,0.0003334651,0.0003775422,0.0002093796,0.00004683696,0.00001338378],"category_scores_gemma":[0.000003956124,0.0001296879,0.00003225214,0.0002352317,0.00005967191,0.0009193079,0.00000999807,0.00009377188,0.000008764735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004326293,"about_ca_system_score_gemma":0.00006275633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008688642,"about_ca_topic_score_gemma":8.446518e-7,"domain_scores_codex":[0.9989737,0.00001703174,0.0001726501,0.0003905792,0.0001735128,0.0002725744],"domain_scores_gemma":[0.9996512,0.00002224011,0.00008586632,0.0001170503,0.00005145925,0.00007216128],"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":[0.00001559485,0.000008824688,0.00002821825,0.00005342346,0.000003194738,0.000001712546,0.0004062843,0.001349378,0.3167746,0.0000031645,0.001183625,0.680172],"study_design_scores_gemma":[0.001038319,0.0001089867,0.0003417476,0.0002841506,0.00002709411,0.0001175116,0.00004036609,0.1021314,0.8862004,0.002620806,0.006462705,0.0006265935],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3371052,0.000353454,0.6570073,0.004566253,0.0001691424,0.0001732193,0.000001119907,0.0001921055,0.0004322711],"genre_scores_gemma":[0.9416565,0.00001106058,0.05595596,0.001700222,0.0003903484,0.00002039522,0.000001987814,0.00001391404,0.0002496302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6795453,"threshold_uncertainty_score":0.528852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01291970886201879,"score_gpt":0.2351088781655279,"score_spread":0.2221891693035092,"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."}}