{"id":"W1656104036","doi":"10.1109/newcas.2004.1359047","title":"An efficient, low-complexity, normalized LMS algorithm for echo cancellation","year":2004,"lang":"en","type":"article","venue":"The 2nd Annual IEEE Northeast Workshop on Circuits and Systems, 2004. NEWCAS 2004.","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Echo (communications protocol); Algorithm; Computer science; Teleconference; Adaptive filter; Least mean squares filter; Filter (signal processing); Telecommunications","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":[0.0004630087,0.000579203,0.000598378,0.0001911446,0.0004797555,0.0001935921,0.0004443115,0.0002371095,0.0000117299],"category_scores_gemma":[0.00001793665,0.0004723701,0.0001284283,0.000363676,0.0001978826,0.0003001895,0.00002780151,0.0004013024,0.00001984692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002630083,"about_ca_system_score_gemma":0.00005922122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002589176,"about_ca_topic_score_gemma":0.0001456483,"domain_scores_codex":[0.9974128,0.00009368065,0.0006662418,0.0005911735,0.0004262194,0.0008098601],"domain_scores_gemma":[0.9985175,0.0001365142,0.0001791342,0.0007047915,0.0001838079,0.0002782842],"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":[0.00003899689,0.0001267056,0.00002635924,0.0001601626,0.000058462,0.00001422543,0.001271568,0.9534559,0.001819982,0.00112137,0.0004029548,0.04150337],"study_design_scores_gemma":[0.008245749,0.001197738,0.001273672,0.003492715,0.000237558,0.0004990035,0.005344534,0.9487172,0.008643145,0.003652519,0.01457614,0.004119966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1438392,0.001965124,0.8461621,0.0001007419,0.001775428,0.002434907,0.0009940374,0.001453034,0.00127544],"genre_scores_gemma":[0.9963122,0.00008563658,0.001816095,0.0000819561,0.0009781175,0.0002195099,0.00008293054,0.0001555189,0.0002680568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.852473,"threshold_uncertainty_score":0.9997728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03689725271583761,"score_gpt":0.2827494242252918,"score_spread":0.2458521715094542,"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."}}