{"id":"W3193389027","doi":"","title":"Audio Scene Monitoring Using Redundant Ad Hoc Microphone Array Networks","year":2022,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Office of Naval Research","keywords":"Computer science; Microphone array; Affine transformation; Principal component analysis; Transformation (genetics); Microphone; Fusion center; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Computer vision; Algorithm; Mathematics; Wireless; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002637221,0.0007064702,0.0004231284,0.0007499898,0.0003094347,0.000534186,0.0008237008,0.0004286765,0.0009425845],"category_scores_gemma":[0.0008933945,0.0002656395,0.0002343279,0.0005176007,0.0002008291,0.001063723,0.000624678,0.0002867734,0.000563555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002667768,"about_ca_system_score_gemma":0.00025211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001455881,"about_ca_topic_score_gemma":0.002432906,"domain_scores_codex":[0.9996114,0.00009191846,0.00001378076,0.0001201944,0.0001284399,0.00003415017],"domain_scores_gemma":[0.9996345,0.0001034353,0.00005466064,0.00006616233,0.0001138251,0.00002734308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000861606,0.0001517894,0.003793108,0.0002031686,0.0001255267,0.0004854724,0.0002174136,0.07620759,0.1833846,0.002091669,0.005121939,0.7273561],"study_design_scores_gemma":[0.00006339773,0.0003374739,0.004172973,0.00002214619,0.00007457259,0.0005118859,0.0001147456,0.9197026,0.06526886,0.002058748,0.007626146,0.00004644465],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09497827,0.0009100885,0.8950465,0.0002114071,0.0001193324,0.00006841601,0.000234167,0.004131246,0.004300664],"genre_scores_gemma":[0.6839222,0.0005897303,0.3109568,0.00013848,0.0001556207,0.0001017783,0.0003579675,0.0001054241,0.003672042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001455881,"threshold_uncertainty_score":0.003153265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01746085222106823,"score_gpt":0.2185345617018971,"score_spread":0.2010737094808289,"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."}}