{"id":"W2139129402","doi":"10.1109/robot.2004.1307286","title":"Localization of simultaneous moving sound sources for mobile robot using a frequency- domain steered beamformer approach","year":2004,"lang":"en","type":"preprint","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Mobile robot; Robot; Acoustic source localization; Frequency domain; Probabilistic logic; Computer vision; Complement (music); Artificial intelligence; Range (aeronautics); Beamforming; Acoustics; Sound (geography); Engineering; 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":[0.0003303587,0.0006723009,0.0004733994,0.0003555303,0.0001530982,0.0004072531,0.0004562065,0.0008247516,0.002361574],"category_scores_gemma":[0.0007037692,0.0003888087,0.0004276164,0.0003388518,0.0003739067,0.0006974489,0.000612552,0.0003964656,0.001401241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001203718,"about_ca_system_score_gemma":0.000357822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005328671,"about_ca_topic_score_gemma":0.001167525,"domain_scores_codex":[0.9997798,0.00006032453,0.00001027361,0.00004967501,0.00008335446,0.00001673386],"domain_scores_gemma":[0.9997267,0.00008933076,0.00003548936,0.00003530906,0.00009576117,0.00001728233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003724488,0.00006737015,0.0005199422,0.0001907557,0.000147074,0.0004136304,0.0002917361,0.1048595,0.5867736,0.005284606,0.001893239,0.299186],"study_design_scores_gemma":[0.0001245095,0.0004170804,0.0008167197,0.00002989289,0.0000950827,0.0008332281,0.0001367642,0.8626203,0.1202336,0.005594144,0.009032671,0.00006605504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006404826,0.00007495734,0.9927273,0.0000481616,0.00002338433,0.000008756947,0.00000897071,0.0003627659,0.0003408239],"genre_scores_gemma":[0.1272667,0.0003171076,0.8687383,0.00008879363,0.00005242187,0.00006340616,0.00007088973,0.00006233985,0.003339944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002361574,"threshold_uncertainty_score":0.007900238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02598173886488693,"score_gpt":0.2722140111097762,"score_spread":0.2462322722448892,"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."}}