{"id":"W3097800283","doi":"","title":"Numerical and Experimental Study of Various Interior Source Identification Methods With Circular Microphone Array","year":2020,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Beamforming; Tikhonov regularization; Microphone array; Deconvolution; Acoustics; Inverse problem; Regularization (linguistics); Microphone; Inverse; Mathematics; Computer science; Directivity; Algorithm; Mathematical analysis; Physics; Telecommunications; Geometry; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.002419632,0.0005805737,0.0006087068,0.0007525138,0.0004618129,0.0005482328,0.0005546025,0.0007374518,0.002516071],"category_scores_gemma":[0.008859078,0.0003202894,0.0003661136,0.000679074,0.0008857792,0.0009095542,0.0007578756,0.0005238631,0.0005701056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002402518,"about_ca_system_score_gemma":0.0003501499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007194908,"about_ca_topic_score_gemma":0.0008184259,"domain_scores_codex":[0.9986681,0.0004177514,0.00007149468,0.0001569819,0.0006342585,0.0000514002],"domain_scores_gemma":[0.9925683,0.004253083,0.0004751685,0.0007305896,0.001826142,0.0001468821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001594667,0.0004509586,0.004197006,0.001525742,0.0001035675,0.0004108759,0.001450047,0.1320457,0.5316306,0.007083329,0.001523063,0.3179844],"study_design_scores_gemma":[0.000113821,0.001174096,0.004524217,0.0001221795,0.00009171299,0.0005220516,0.0004764437,0.6948618,0.2915236,0.00232797,0.004122929,0.0001392495],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2303661,0.0007712385,0.761007,0.0002450673,0.0001351542,0.0001556557,0.000158122,0.0009051476,0.006256509],"genre_scores_gemma":[0.490961,0.0004708793,0.5061677,0.00005538747,0.00003142134,0.0001606653,0.0001556231,0.0001010179,0.001896315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002516071,"threshold_uncertainty_score":0.0127964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01625415069402198,"score_gpt":0.2676999224593029,"score_spread":0.2514457717652809,"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."}}