{"id":"W2173129408","doi":"10.1121/1.4934954","title":"Direction-of-arrival estimation of passive acoustic sources in reverberant environments based on the Householder transformation","year":2015,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Short-time Fourier transform; Transformation (genetics); Reverberation; Microphone array; Direction of arrival; Acoustics; Computer science; Noise (video); Microphone; Fourier transform; SIGNAL (programming language); Range (aeronautics); Frequency domain; Speech recognition; Algorithm; Mathematics; Telecommunications; Physics; Mathematical analysis; Artificial intelligence; Antenna (radio); Engineering; Loudspeaker; Fourier analysis; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003492604,0.0005841232,0.0007557444,0.0005248349,0.0001898262,0.0004812324,0.0003984964,0.0004862468,0.001175625],"category_scores_gemma":[0.001343829,0.0003127977,0.0004843931,0.0004614163,0.0003165138,0.0007830794,0.0004852689,0.0005626858,0.0006872365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001905995,"about_ca_system_score_gemma":0.0003638729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009742127,"about_ca_topic_score_gemma":0.001551877,"domain_scores_codex":[0.999738,0.00007795231,0.00001048713,0.00004860239,0.0001082397,0.00001674614],"domain_scores_gemma":[0.999683,0.0001615142,0.00003919843,0.00003041443,0.00007204161,0.00001374753],"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":[0.0003023943,0.0000957009,0.001491188,0.0001723219,0.0001424735,0.0002494583,0.0001970865,0.3740185,0.1094395,0.01261151,0.001529817,0.49975],"study_design_scores_gemma":[0.00001567852,0.0000937397,0.0009283688,0.000007880162,0.00002295077,0.0001864123,0.00002831845,0.977212,0.01558957,0.003722636,0.002161707,0.00003079091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008575954,0.00009327206,0.9905325,0.0000262506,0.00001414931,0.000009393562,0.00001239901,0.0002058629,0.0005302425],"genre_scores_gemma":[0.2197457,0.0006397599,0.7754644,0.00006210514,0.00008427914,0.00005980072,0.0001277423,0.0001316071,0.003684683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001175625,"threshold_uncertainty_score":0.003932834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01680925438606663,"score_gpt":0.234105301752727,"score_spread":0.2172960473666604,"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."}}