{"id":"W2534542549","doi":"10.1109/taslp.2016.2618003","title":"Superdirective Beamforming Based on the Krylov Matrix","year":2016,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Audio Speech and Language Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Krylov subspace; Beamforming; Computer science; Dimension (graph theory); Parametric statistics; Directivity; Matrix (chemical analysis); Algorithm; Noise (video); Adaptive beamformer; Acoustics; Mathematics; Telecommunications; Physics; Antenna (radio); Iterative method; 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":[0.0004341132,0.0003270639,0.0002473971,0.0002687816,0.0008888461,0.0004226789,0.0007747001,0.0001165138,0.00009897576],"category_scores_gemma":[0.0001119957,0.0001875669,0.0001164583,0.0006453069,0.0001368062,0.0007786999,0.0000158891,0.0003394927,0.00006610742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001045844,"about_ca_system_score_gemma":0.0001717367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001856472,"about_ca_topic_score_gemma":0.000022638,"domain_scores_codex":[0.9979943,0.00008273506,0.0002744023,0.0006540191,0.0004466777,0.0005478338],"domain_scores_gemma":[0.9984179,0.0004661595,0.0001265395,0.0007163166,0.0001032974,0.0001698424],"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":[0.00004099007,0.00009786674,0.00005910388,0.00003857938,0.00001698024,0.00003864934,0.001047802,0.00008292022,0.03479175,0.00002035909,0.00005392443,0.9637111],"study_design_scores_gemma":[0.001137938,0.0002633315,0.0002147823,0.0008068098,0.00003814873,0.0001179527,0.0006320887,0.006245208,0.9883006,0.0008995756,0.0007474332,0.00059613],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06468076,0.0005191849,0.9265519,0.006047189,0.0002477967,0.0002421056,0.00001258388,0.0004170154,0.00128152],"genre_scores_gemma":[0.9279098,0.00003375085,0.06956443,0.001379668,0.00012612,0.00004606909,6.772049e-7,0.00003359298,0.0009058994],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9631149,"threshold_uncertainty_score":0.7648758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01145732930369991,"score_gpt":0.2560578839495354,"score_spread":0.2446005546458355,"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."}}