{"id":"W2952296905","doi":"10.48550/arxiv.1402.4160","title":"Maximizing the Signal-to-Alias Ratio in Non-Uniform Filter Banks for Acoustic Echo Cancellation","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Echo (communications protocol); Return loss; Bandwidth (computing); Group delay and phase delay; Filter bank; Adaptive filter; Computer science; Alias; Filter (signal processing); Anti-aliasing filter; Root-raised-cosine filter; Electronic engineering; Acoustics; Control theory (sociology); Telecommunications; Algorithm; Engineering; Low-pass filter; Physics","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":[0.0002312178,0.0003600856,0.0003346359,0.0002787973,0.00009449894,0.00004903298,0.0006008741,0.000267046,0.0000296098],"category_scores_gemma":[0.00003403318,0.0003755327,0.0001307617,0.0002674082,0.00004521423,0.0001516876,0.0003415931,0.0005586956,0.00002129108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006022407,"about_ca_system_score_gemma":0.00003385466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007322046,"about_ca_topic_score_gemma":0.0002156082,"domain_scores_codex":[0.9987351,0.00003331558,0.0002447448,0.0005377187,0.00006470121,0.0003843805],"domain_scores_gemma":[0.9989758,0.0001823739,0.0001065911,0.000557365,0.00009557721,0.00008229231],"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":[0.00003502034,0.000007747883,0.0001180492,0.0001796731,0.0000300326,0.000009753493,0.000176185,0.9948144,0.00290004,0.0007112985,0.0004354702,0.0005823604],"study_design_scores_gemma":[0.0002745896,0.00004142317,0.0005465919,0.0002633526,0.00004800244,8.795128e-7,0.00004790829,0.9855029,0.003788803,0.008200259,0.0008271525,0.0004581257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06913282,0.000013299,0.9277556,0.00002517147,0.0002590801,0.001074682,0.00005084557,0.0003828809,0.001305649],"genre_scores_gemma":[0.9874543,0.00005540092,0.01153147,0.00006085465,0.0001395012,0.00002715333,0.00003541648,0.00007671033,0.0006192208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9183214,"threshold_uncertainty_score":0.9998696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05095084215519059,"score_gpt":0.1963947306156303,"score_spread":0.1454438884604397,"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."}}