{"id":"W1557290754","doi":"","title":"Coherent and incoherent interference reduction using a subband tradeoff beamformer","year":2011,"lang":"en","type":"article","venue":"European Signal Processing Conference","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Interference (communication); Computer science; Reduction (mathematics); Perspective (graphical); Distortion (music); Noise (video); Noise reduction; Beamforming; Speech recognition; Set (abstract data type); Acoustics; Signal-to-noise ratio (imaging); Algorithm; Mathematics; Artificial intelligence; Telecommunications; Physics; Bandwidth (computing); 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004659744,0.0003263823,0.0002599004,0.0001629516,0.0003828967,0.0006670437,0.0008118043,0.00005436286,0.00008444123],"category_scores_gemma":[0.00001778057,0.0002906882,0.0000472875,0.0003666646,0.0002242877,0.001487384,0.0003381567,0.0003341882,0.00003194283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004816013,"about_ca_system_score_gemma":0.0002238464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002218783,"about_ca_topic_score_gemma":0.000003464336,"domain_scores_codex":[0.9978706,0.0001734285,0.0004389109,0.0007431559,0.0002985628,0.0004752971],"domain_scores_gemma":[0.9989344,0.00001617803,0.0003034339,0.0002987859,0.0002158242,0.0002314101],"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":[0.00005427755,0.0001568402,0.000923174,0.0001713211,0.00002432693,0.00004902301,0.01092554,0.00000899949,0.1499981,0.0006574015,0.00004784477,0.8369831],"study_design_scores_gemma":[0.003623993,0.001819443,0.02043213,0.00522622,0.00020557,0.002655643,0.003422512,0.08721618,0.8482038,0.02087414,0.002009303,0.004311045],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2445007,0.0005999456,0.7395241,0.00006438941,0.0001507556,0.0001607044,0.000001193963,0.0002621903,0.01473609],"genre_scores_gemma":[0.9302889,0.00002100256,0.06931349,0.0001003138,0.0001018484,0.000004546046,0.00000162071,0.0000278928,0.000140408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8326721,"threshold_uncertainty_score":0.9999545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08395056231313583,"score_gpt":0.2503585055145274,"score_spread":0.1664079432013916,"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."}}