{"id":"W1843447117","doi":"","title":"System identification with adaptive lattice filters for speech data","year":2005,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Lattice phase equaliser; Adaptive filter; Recursive least squares filter; Least mean squares filter; Lattice (music); Algorithm; Kernel adaptive filter; Computer science; Filter (signal processing); Speech recognition; Computational complexity theory; Square lattice; System identification; Speech processing; Mathematics; Filter design; Acoustics; Data modeling; Statistical physics; Physics; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001030277,0.0004355722,0.0005239891,0.0005115684,0.000414859,0.0007155505,0.0006001301,0.0006093337,0.001525318],"category_scores_gemma":[0.003231208,0.0002951512,0.0005612313,0.0007262913,0.0004465741,0.00100889,0.0005402821,0.001075512,0.0009570481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005241875,"about_ca_system_score_gemma":0.001102228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004249687,"about_ca_topic_score_gemma":0.00452749,"domain_scores_codex":[0.9993269,0.0002218823,0.00003697985,0.0001266937,0.0002383691,0.00004923901],"domain_scores_gemma":[0.9993106,0.0003632399,0.00005204665,0.00008488126,0.0001708345,0.00001833114],"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":[0.0004557876,0.0001109177,0.001564636,0.0001980517,0.0001021743,0.0001374368,0.0002922531,0.4519399,0.06178261,0.02532809,0.001483128,0.4566049],"study_design_scores_gemma":[0.00001045451,0.0000286485,0.0001775752,0.000006050396,0.000004527365,0.00003768897,0.00001130248,0.990945,0.00549309,0.002263944,0.001010832,0.00001097354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006519223,0.0000574627,0.9927,0.00003300026,0.00001457186,0.00001767551,0.000022808,0.0003798714,0.0002553767],"genre_scores_gemma":[0.2742175,0.0002277269,0.722595,0.00005204358,0.0000281418,0.0002277834,0.0002009127,0.00007758479,0.002373327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004249687,"threshold_uncertainty_score":0.008449912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03164096212042832,"score_gpt":0.2419449236598102,"score_spread":0.2103039615393819,"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."}}