{"id":"W4404577122","doi":"10.23919/eusipco63174.2024.10715294","title":"Normalized Multichannel Frequency-Domain LMS Filter With Nearest Kronecker Product Decomposition for Blind Identification of Low-Rank Acoustic Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"National Key Research and Development Program of China; Department of Science and Technology of Sichuan Province; National Science Foundation","keywords":"Kronecker product; Computer science; Frequency domain; Adaptive filter; Least mean squares filter; Kronecker delta; Filter (signal processing); Speech recognition; Rank (graph theory); Mathematics; Algorithm; Physics; Computer vision","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":[0.0001993505,0.0002042688,0.0002204835,0.000171739,0.000042104,0.00007918345,0.0001323138,0.00006339261,0.00002075519],"category_scores_gemma":[0.00002084174,0.0001737679,0.00005311555,0.0001773958,0.0000571793,0.0003801197,0.00001741976,0.0001057021,0.00002063945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001085258,"about_ca_system_score_gemma":0.00001818589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001756398,"about_ca_topic_score_gemma":0.00001683792,"domain_scores_codex":[0.9988776,0.00001939491,0.0004126678,0.0003010385,0.0001605934,0.0002287172],"domain_scores_gemma":[0.9993777,0.00007125982,0.00005381633,0.0003122623,0.0001411682,0.00004380099],"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":[0.00006503685,0.00002924378,0.00001333035,0.00159172,0.00008423335,0.000006359605,0.0002477837,0.09294662,0.9030469,0.001432409,0.0002220713,0.0003142617],"study_design_scores_gemma":[0.0006841632,0.0001524577,0.0002399633,0.0008890007,0.00007396731,0.00003415486,0.0001141257,0.5967,0.3991048,0.001325554,0.0002566674,0.0004250923],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1146384,0.0003852232,0.8823659,0.00002693952,0.0003832332,0.001176235,0.0001077894,0.0007551514,0.0001611466],"genre_scores_gemma":[0.8914539,0.00001743109,0.1076613,0.000003168202,0.0001087576,0.000441179,0.0000946139,0.00007381885,0.0001457975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7768155,"threshold_uncertainty_score":0.7086051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01374977866913022,"score_gpt":0.2651775377110232,"score_spread":0.251427759041893,"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."}}