{"id":"W1597266963","doi":"10.5281/zenodo.38304","title":"Echo Cancellation Using A Variable Step-Size Nlms Algorithm","year":2004,"lang":"en","type":"article","venue":"INFM-OAR (INFN Catania)","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Convergence (economics); Echo (communications protocol); Variable (mathematics); Least mean squares filter; Algorithm; Residual; Computer science; Adaptive filter; Scheme (mathematics); 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":[0.0001463649,0.0003072579,0.0002802754,0.000106084,0.0001138319,0.00006259623,0.0002627207,0.0001707806,0.00007483126],"category_scores_gemma":[0.00005811751,0.0003627952,0.00005940649,0.0003898123,0.00005354593,0.0004710321,0.00008742833,0.0003151082,0.00005925865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008661266,"about_ca_system_score_gemma":0.00008763342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00040577,"about_ca_topic_score_gemma":0.00004578022,"domain_scores_codex":[0.9985858,0.00001559306,0.0003482013,0.0003198909,0.000255632,0.0004748585],"domain_scores_gemma":[0.9991499,0.00005983444,0.00007737355,0.0005022128,0.00009269692,0.0001180237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002010156,0.00005753702,0.0001429022,0.0001656865,0.0001135146,0.00007878383,0.0006279694,0.6764357,0.2888841,0.002635302,0.000713578,0.03012484],"study_design_scores_gemma":[0.003138708,0.0003105011,0.001051762,0.001078891,0.0001579619,0.000300045,0.0002494406,0.460449,0.3780117,0.02841944,0.1236413,0.003191214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02617096,0.000302095,0.9668762,0.0000202941,0.0005528548,0.0003940504,0.0001054009,0.001863281,0.003714838],"genre_scores_gemma":[0.30397,0.00006036456,0.6952973,0.00007003276,0.0002931478,0.00003913156,0.0000234751,0.0001182481,0.0001283187],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.277799,"threshold_uncertainty_score":0.9998824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01335282652974451,"score_gpt":0.2344950045888709,"score_spread":0.2211421780591264,"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."}}