{"id":"W2104257868","doi":"10.1109/tsmcb.2002.804369","title":"Self-localizing dynamic microphone arrays","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Microphone; Microphone array; Orientation (vector space); Observability; Computer science; Acoustics; Estimator; Speech recognition; Mathematics; Physics; Sound pressure; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002005747,0.0002076782,0.0003036932,0.000103108,0.0004544242,0.0002831383,0.0002417494,0.00008229547,0.00001242802],"category_scores_gemma":[9.962562e-7,0.0001766231,0.00006704844,0.0003668225,0.00006805633,0.0001456597,0.000004719091,0.0001713064,0.0001595938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002873798,"about_ca_system_score_gemma":0.00001007717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007373633,"about_ca_topic_score_gemma":0.000006866007,"domain_scores_codex":[0.9986662,0.00006671436,0.0004177463,0.0004683457,0.0001392756,0.000241676],"domain_scores_gemma":[0.9991151,0.00003986266,0.0001415342,0.0004858394,0.00004715553,0.0001705092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002721175,0.0005497461,0.00002295814,0.001227715,0.00009418584,0.000005944108,0.001209847,0.0003532774,0.006595099,0.001683514,0.002045447,0.9862096],"study_design_scores_gemma":[0.0004177928,0.00008870101,0.00001627801,0.000420667,0.00007818998,0.0002193635,0.00007944938,0.04596839,0.004105989,0.000141751,0.947973,0.0004904498],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007817834,0.03639382,0.9602203,0.0003819313,0.0002195217,0.0006717246,0.000007038423,0.0001546795,0.001169209],"genre_scores_gemma":[0.7606902,0.1718912,0.06046518,0.0006197816,0.0001607669,0.001071411,0.000002794343,0.00004864795,0.005050032],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9857191,"threshold_uncertainty_score":0.7202482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01819342294778886,"score_gpt":0.2345650866873736,"score_spread":0.2163716637395847,"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."}}