{"id":"W2151149289","doi":"10.1109/ccece.2008.4564508","title":"A new adaptive beamformer for optimal acoustic echo and noise cancellation with less computational load","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Echo (communications protocol); Computer science; Adaptive beamformer; Adaptive filter; Noise (video); Active noise control; Computational complexity theory; Scheme (mathematics); Channel (broadcasting); Path (computing); Speech recognition; Acoustics; Beamforming; Algorithm; Telecommunications; Mathematics; Artificial intelligence; Physics; Computer network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006663285,0.0007137409,0.0005718948,0.0004549421,0.0001750904,0.0003515555,0.0007978958,0.0007759697,0.002265843],"category_scores_gemma":[0.001156357,0.0002996878,0.0004569545,0.0005458499,0.0003657526,0.0009313644,0.0006069602,0.000637976,0.0008727051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001984868,"about_ca_system_score_gemma":0.0006495784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009037697,"about_ca_topic_score_gemma":0.002194487,"domain_scores_codex":[0.9994456,0.0001097198,0.00003042084,0.000107787,0.0002709736,0.00003551926],"domain_scores_gemma":[0.9994903,0.0001566571,0.00004485916,0.00005750601,0.000221077,0.00002974339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003092508,0.0001048046,0.001242746,0.0003077257,0.0001753003,0.0001362176,0.0001344958,0.07395367,0.2617576,0.01906445,0.003582412,0.6392313],"study_design_scores_gemma":[0.0001331935,0.0004065819,0.001453418,0.00004297601,0.0001343232,0.0009338462,0.00004043897,0.899112,0.06713346,0.004904244,0.02562995,0.00007555642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001958348,0.000139921,0.9971973,0.00003472448,0.000044489,0.00001257797,0.00000804772,0.0001169407,0.0004876104],"genre_scores_gemma":[0.06165741,0.0003315863,0.9348612,0.0001361109,0.00009925914,0.00006973011,0.00007086834,0.00003530493,0.0027385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002265843,"threshold_uncertainty_score":0.007579982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0195251886815594,"score_gpt":0.1959197133954592,"score_spread":0.1763945247138999,"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."}}