{"id":"W2098651198","doi":"10.1109/tsmcb.2004.826398","title":"Enhanced Sound Localization","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Microphone; Reverberation; Acoustic source localization; Directivity; Acoustics; Orientation (vector space); Sound localization; Ranging; Microphone array; Computer science; Sound (geography); Noise-canceling microphone; Mathematics; Physics; Sound pressure; Telecommunications; Geometry","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":[0.0002423668,0.0003661323,0.0003470614,0.0002059663,0.0004002693,0.000628489,0.000481979,0.0002018041,0.00001990371],"category_scores_gemma":[0.000006637801,0.0003653058,0.000100085,0.0004829836,0.0001518524,0.0003327664,0.000008871963,0.0002876727,0.0002193406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001264929,"about_ca_system_score_gemma":0.000101105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009113752,"about_ca_topic_score_gemma":0.0001081326,"domain_scores_codex":[0.9975361,0.00008010317,0.0005448037,0.0007233838,0.0005353895,0.0005801755],"domain_scores_gemma":[0.9986323,0.00006558334,0.0001857821,0.0006403696,0.0001585452,0.0003174001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001490813,0.002191446,0.0001998649,0.000904253,0.0005389836,0.000162185,0.01291485,0.7424109,0.02177163,0.05323931,0.001740067,0.1637774],"study_design_scores_gemma":[0.006889295,0.001642885,0.0003433074,0.001586575,0.0002552076,0.0004875312,0.0009587809,0.05114966,0.8868001,0.02102642,0.02572914,0.003131174],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03331189,0.0005308027,0.9602667,0.0002285368,0.00143566,0.0003571589,0.0000086415,0.0003041009,0.003556489],"genre_scores_gemma":[0.9938855,0.0003096893,0.003072172,0.0003446616,0.0001508891,0.0000567506,0.000003167695,0.00004056315,0.002136649],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9605736,"threshold_uncertainty_score":0.9998799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01668988838778912,"score_gpt":0.2359552121620396,"score_spread":0.2192653237742505,"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."}}