{"id":"W2084528923","doi":"10.1121/1.4913459","title":"A multistage minimum variance distortionless response beamformer for noise reduction","year":2015,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Adaptive beamformer; Minimum-variance unbiased estimator; Noise (video); Beamforming; Channel (broadcasting); Computer science; Microphone array; Mathematics; Uncorrelated; Reduction (mathematics); Noise reduction; Acoustics; Microphone; Telecommunications; Physics; Statistics; Sound pressure; Mean squared error; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001412472,0.00009358875,0.0001978788,0.00001445463,0.0002003194,0.00003839827,0.0009517831,0.00004264903,0.000002112094],"category_scores_gemma":[0.0006156407,0.00005007557,0.000251122,0.0002911291,0.0003515185,0.0002778159,0.0001385188,0.000210068,0.000001474794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001074167,"about_ca_system_score_gemma":0.0002734616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007721492,"about_ca_topic_score_gemma":6.881201e-8,"domain_scores_codex":[0.9987893,0.0001379607,0.0003512547,0.00009508146,0.0004287869,0.0001976217],"domain_scores_gemma":[0.9982351,0.0003905527,0.0005898186,0.0003098666,0.0003663698,0.0001082655],"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":[0.003865362,0.0006099013,0.00007651333,0.0001193215,0.0002771304,0.000002176941,0.01406394,0.02244162,0.6874032,0.00003728976,0.1402132,0.1308904],"study_design_scores_gemma":[0.005541132,0.003006017,0.003178045,0.000546477,0.0006925769,0.001066712,0.01233944,0.7334353,0.1620023,0.01732381,0.06002567,0.00084252],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02029812,0.0002074034,0.9673234,0.01157684,0.0004544994,0.0001051037,0.000003820596,0.0000109792,0.00001982612],"genre_scores_gemma":[0.4349906,0.00003490103,0.5637683,0.0007218855,0.0002197094,0.00000208383,1.334718e-7,0.000007984445,0.0002543805],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7109936,"threshold_uncertainty_score":0.2042022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0219434077248098,"score_gpt":0.2750939701824319,"score_spread":0.2531505624576221,"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."}}