{"id":"W4414758184","doi":"10.1109/taslpro.2025.3617242","title":"A Temporal–Spatial Joint High-Gain Beamforming Method in the STFT Domain Based on Kronecker Product Filters","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Audio Speech and Language Processing","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":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Kronecker product; Beamforming; Robustness (evolution); Kronecker delta; Noise (video); White noise; Filter (signal processing); Sensitivity (control systems); Frequency domain","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":[0.001444366,0.0003310869,0.0003314693,0.0005862009,0.000558266,0.0005106355,0.000531155,0.00009897504,0.00002238959],"category_scores_gemma":[0.00004141343,0.0002454162,0.00009575224,0.001185688,0.00007910214,0.000504096,0.000007651324,0.0006110265,0.000007157599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001061102,"about_ca_system_score_gemma":0.0002779248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002550695,"about_ca_topic_score_gemma":0.0002794206,"domain_scores_codex":[0.9976304,0.0002660738,0.0004118473,0.0007520458,0.0004327493,0.0005069167],"domain_scores_gemma":[0.9989862,0.0001966045,0.0001489054,0.000528346,0.00005553169,0.00008435889],"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":[0.00006345794,0.0002174471,0.00002454341,0.0001531473,0.00001422501,0.00008666599,0.003211571,0.002917364,0.01370688,0.0000154081,0.00005816574,0.9795311],"study_design_scores_gemma":[0.002260444,0.0002671256,0.0002967326,0.001295058,0.00005162611,0.00007899275,0.002301541,0.1038956,0.8873795,0.0009368197,0.0005386608,0.0006978982],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02231038,0.0004218295,0.9712093,0.004512595,0.0002394811,0.000415153,0.000006683184,0.0001458218,0.000738696],"genre_scores_gemma":[0.7110853,0.000006958804,0.2863184,0.002283984,0.0000525698,0.00005777631,0.000002700259,0.00001520744,0.000177126],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9788332,"threshold_uncertainty_score":0.9999998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01020571976337821,"score_gpt":0.2666923017617255,"score_spread":0.2564865819983473,"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."}}