{"id":"W2545745909","doi":"10.1109/nnsp.1995.514919","title":"Nonlinear echo cancellation using a partial adaptive time delay neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Loudspeaker; Adaptive filter; Microphone; Computer science; Artificial neural network; Speech recognition; Nonlinear system; Echo (communications protocol); Impulse response; Least mean squares filter; Acoustics; Finite impulse response; Artificial intelligence; Algorithm; Mathematics; 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":[],"consensus_categories":[],"category_scores_codex":[0.00004465228,0.0001709941,0.0001537334,0.0000393783,0.00006253485,0.00001895212,0.00009253321,0.00007144126,0.0004796753],"category_scores_gemma":[0.000006257182,0.0001771278,0.00004512906,0.0001689131,0.0000264921,0.0002072753,0.00003421495,0.0001501508,0.00008754677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001257065,"about_ca_system_score_gemma":0.000003037889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001636816,"about_ca_topic_score_gemma":0.000008471002,"domain_scores_codex":[0.9992012,0.00001726571,0.0001845235,0.0001694291,0.0001060625,0.0003215534],"domain_scores_gemma":[0.9996743,0.00002854612,0.00002951827,0.0001741208,0.00003107007,0.0000625049],"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":[0.000006967406,0.000007571784,0.0000410185,0.000003864734,0.00001604096,0.000009832992,0.00005202011,0.985728,0.007795228,0.00007600885,0.001428231,0.004835223],"study_design_scores_gemma":[0.00009548407,0.00004118017,0.00001476562,0.00002070047,0.00000863248,0.00001074617,0.000003298872,0.9880297,0.006959883,0.0001171724,0.004488,0.0002104411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07467467,0.0002778357,0.9104285,0.0000194418,0.0002819264,0.0003052431,0.0000129907,0.002399914,0.01159954],"genre_scores_gemma":[0.687408,0.00002067484,0.3114623,0.00004085582,0.0005244312,0.0000111096,0.000004740921,0.00005779364,0.0004700552],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6127334,"threshold_uncertainty_score":0.722306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03163600709867491,"score_gpt":0.2364840683409034,"score_spread":0.2048480612422285,"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."}}