{"id":"W3120369513","doi":"10.3390/s21020532","title":"Towards Robust Multiple Blind Source Localization Using Source Separation and Beamforming","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Beamforming; Blind signal separation; Microphone array; Acoustic source localization; Computer science; Weighting; Source separation; Direction of arrival; Microphone; Interference (communication); Angle of arrival; Noise (video); Acoustics; Algorithm; Artificial intelligence; Sound (geography); Telecommunications","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":[0.0001396125,0.0001097528,0.0001155272,0.00007080651,0.0002900598,0.0003122111,0.0001137585,0.00006557454,0.000004907648],"category_scores_gemma":[0.0001290218,0.0001133347,0.00002945814,0.0004085178,0.00003120656,0.0004224634,0.0001216599,0.00008334021,0.000006007821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003543727,"about_ca_system_score_gemma":0.00008900269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003130758,"about_ca_topic_score_gemma":0.00002044705,"domain_scores_codex":[0.99905,0.00004276716,0.0001680218,0.0003222968,0.0002004143,0.0002165097],"domain_scores_gemma":[0.9994882,0.00003667814,0.00008388118,0.0001944542,0.0001195999,0.00007718521],"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":[0.00001058295,0.0000363804,0.004288035,0.00005455473,0.0000173731,0.00002523369,0.004312322,0.7960733,0.02809741,0.0000764067,0.0000541762,0.1669542],"study_design_scores_gemma":[0.0003005651,0.000009380989,0.000134911,0.00003621552,0.000006745159,0.00008842578,0.0003448956,0.8162736,0.1803111,0.0001064847,0.002252205,0.0001355024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4448628,0.0001237721,0.554621,0.00009787508,0.00006941493,0.00003411956,2.777105e-7,0.00006528611,0.0001255276],"genre_scores_gemma":[0.797983,0.00001432701,0.2011288,0.0002871245,0.000119436,0.000001048882,0.000006543426,0.00001386354,0.0004458406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3534922,"threshold_uncertainty_score":0.4621653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03327774572101128,"score_gpt":0.2739286567683367,"score_spread":0.2406509110473254,"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."}}