{"id":"W4392904375","doi":"10.1109/icassp48485.2024.10448270","title":"A Steered Response Power Approach with Bilinear Prediction-Based Trade-Off Prewhitening for Speaker Localization","year":2024,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Research and Development; National Science Foundation","keywords":"Bilinear interpolation; Microphone; Reverberation; Computer science; Speech recognition; Filter (signal processing); Linear prediction; Noise (video); Microphone array; Speech processing; Algorithm; Acoustics; Artificial intelligence; Telecommunications; Computer vision","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":[0.0005109221,0.0001428789,0.0001153912,0.0001701968,0.0001377966,0.000494099,0.0002467532,0.00006297053,0.00002640828],"category_scores_gemma":[0.00006301847,0.0001016395,0.00005470862,0.0007209483,0.00003590777,0.0005206158,0.00002476295,0.00008856973,0.00001129746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004613339,"about_ca_system_score_gemma":0.0003126886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001283116,"about_ca_topic_score_gemma":7.470954e-7,"domain_scores_codex":[0.9987286,0.00005257782,0.0001941197,0.000497235,0.0002814456,0.0002460595],"domain_scores_gemma":[0.9993554,0.000187818,0.0000338105,0.0002779308,0.00006447925,0.000080498],"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":[0.00920288,0.002304988,0.005459205,0.002597532,0.0007266245,0.0002513731,0.01980917,0.3500064,0.0402151,0.01177114,0.0544643,0.5031913],"study_design_scores_gemma":[0.0006071692,0.0002802372,0.0002303436,0.0001048671,0.00001286807,0.00002193468,0.00006142453,0.9519422,0.02158587,0.00008722706,0.02490824,0.0001575993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005119844,0.0002722171,0.9907858,0.001090239,0.0001233404,0.0003634755,0.000007629163,0.0007406176,0.001496877],"genre_scores_gemma":[0.6254591,9.71885e-7,0.371917,0.0004780312,0.00007671772,0.00006878541,0.00001794969,0.00002875529,0.001952727],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6203392,"threshold_uncertainty_score":0.4764608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01185362899361515,"score_gpt":0.2331666700069688,"score_spread":0.2213130410133536,"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."}}