{"id":"W3124046719","doi":"10.1016/j.sigpro.2021.108012","title":"Statistical analysis of narrowband active noise control using a simplified variable step-size FXLMS algorithm","year":2021,"lang":"en","type":"article","venue":"Signal Processing","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; Japan Society for the Promotion of Science; Higher Education Discipline Innovation Project; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Least mean squares filter; Active noise control; Mean squared error; Residual; Noise (video); Narrowband; Control theory (sociology); Mathematics; Steady state (chemistry); Filter (signal processing); Mean square; Algorithm; Adaptive filter; Statistics; Computer science; Control (management); Telecommunications; 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":[0.0001448426,0.0002256594,0.0005625726,0.0001586819,0.00009370352,0.00005436952,0.0001224006,0.0001014148,0.000189205],"category_scores_gemma":[0.0001009642,0.0002434389,0.00008586208,0.001027214,0.00008201498,0.0002842174,0.00004187651,0.0002303763,9.005402e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001407399,"about_ca_system_score_gemma":0.0001197865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001797224,"about_ca_topic_score_gemma":0.000002958338,"domain_scores_codex":[0.9986532,0.00005303255,0.0003830915,0.0003097536,0.0002600946,0.0003408292],"domain_scores_gemma":[0.9990443,0.0002840862,0.0001101104,0.0001803856,0.0002856127,0.00009551249],"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":[0.00006985157,0.00007419358,0.0001732795,0.000264642,0.001048634,0.00007631487,0.0002707924,0.24438,0.6722258,0.0003064954,0.00002507223,0.08108488],"study_design_scores_gemma":[0.00041245,0.00002926202,0.0005634958,0.0001327074,0.0006804647,0.000009598631,0.0001420638,0.9157471,0.08053114,0.001368235,0.0001025696,0.0002808884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01529115,0.0003013161,0.9832504,0.000004374011,0.000033174,0.0001155685,0.0002802501,0.0002690704,0.0004547359],"genre_scores_gemma":[0.6432903,0.000003066051,0.3565738,0.00001969232,0.00003468668,0.000009824328,0.00001919693,0.00003379582,0.00001557621],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6713671,"threshold_uncertainty_score":0.9927152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01283814477305151,"score_gpt":0.2622837299517864,"score_spread":0.2494455851787349,"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."}}