{"id":"W1920987940","doi":"10.1109/icassp.1986.1168903","title":"Time delay estimation via generalized correlation with adaptive spatial prefiltering","year":2005,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Adaptive beamformer; Beamforming; Interference (communication); Computer science; Minimum-variance unbiased estimator; Spatial correlation; Algorithm; Noise (video); Reduction (mathematics); Correlation; Variance (accounting); Control theory (sociology); Mathematics; Statistics; Mean squared error; Telecommunications; Artificial intelligence; Image (mathematics)","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":[0.000081524,0.00009192927,0.00008151196,0.00005315712,0.00009875331,0.0001073426,0.0001740408,0.00003206324,0.00007400218],"category_scores_gemma":[0.000006045675,0.00007141197,0.00001675237,0.0001471171,0.0000178398,0.000986815,0.00004843273,0.00005390825,0.0002166311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004426765,"about_ca_system_score_gemma":0.00003593174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002911002,"about_ca_topic_score_gemma":0.00002611078,"domain_scores_codex":[0.9993151,0.00001830069,0.0001240563,0.0002116961,0.0001784546,0.0001523558],"domain_scores_gemma":[0.9996492,0.00001745295,0.00007384427,0.0001562237,0.00005461553,0.00004869582],"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":[0.00003199029,0.00002926766,0.00009718586,0.000003277508,0.00001229611,0.000004364474,0.0004333273,0.1628544,0.009021359,0.0004151385,0.000154211,0.8269432],"study_design_scores_gemma":[0.0003189122,0.00007113,0.0003400704,0.00001499371,0.000003946989,0.00002984493,0.00000163166,0.9370676,0.06167401,0.0002389062,0.0001258151,0.0001131094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04342333,0.00001830965,0.9526955,0.0002815087,0.00004176622,0.00008658625,2.035133e-7,0.0002179409,0.003234877],"genre_scores_gemma":[0.4933474,3.518595e-7,0.506032,0.000123652,0.00004892596,0.000004335717,0.000002528993,0.000004057677,0.0004367499],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.82683,"threshold_uncertainty_score":0.2912096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009088012742180986,"score_gpt":0.2136306752385739,"score_spread":0.2045426624963929,"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."}}