{"id":"W2799970673","doi":"10.18280/mmep.040407","title":"Blind source separation of indoor mobile voice sources","year":2017,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lanzhou Jiaotong University","keywords":"Blind signal separation; Separation (statistics); Computer science; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003700278,0.0006429128,0.0006365336,0.0007831335,0.0003144515,0.0004350222,0.0005519389,0.0008039702,0.0007746621],"category_scores_gemma":[0.001140912,0.0001889302,0.0005885605,0.0005765681,0.0003752423,0.0009517146,0.0007352133,0.0004493469,0.000381607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002801015,"about_ca_system_score_gemma":0.00049726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001503365,"about_ca_topic_score_gemma":0.001030975,"domain_scores_codex":[0.9996217,0.00008232125,0.00001867196,0.00008458666,0.0001421859,0.0000504217],"domain_scores_gemma":[0.9996419,0.0001370422,0.00003935053,0.00003488492,0.0001284119,0.00001842336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001084879,0.0001136305,0.002488876,0.0005782158,0.0001559384,0.0007945843,0.000355581,0.2387044,0.2130282,0.01595149,0.003055142,0.5236891],"study_design_scores_gemma":[0.00006342955,0.0001279639,0.001736018,0.00002058732,0.00004605594,0.0004218005,0.00008547165,0.9232846,0.06681889,0.004693225,0.002635577,0.0000662864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08017497,0.0008481139,0.915678,0.00008687605,0.0001036252,0.00003162085,0.00005439319,0.0007866833,0.002235687],"genre_scores_gemma":[0.8317372,0.000612848,0.1629802,0.00006404807,0.0001029011,0.00005824874,0.0002197413,0.00007200205,0.004152774],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001503365,"threshold_uncertainty_score":0.002989173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02656013797083707,"score_gpt":0.2532751210199634,"score_spread":0.2267149830491263,"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."}}