{"id":"W1910023617","doi":"10.5539/mas.v9n6p310","title":"Improvement of Microphone Array Characteristics for Speech Capturing","year":2015,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Education and Science of the Russian Federation","keywords":"Computer science; Microphone; Noise-canceling microphone; Directivity; Microphone array; Interference (communication); Noise (video); SIGNAL (programming language); Speech recognition; Adaptive beamformer; Acoustics; Speech enhancement; Frequency domain; Domain (mathematical analysis); Time domain; Background noise; Artificial intelligence; Telecommunications; Beamforming; Computer vision; Mathematics; Physics; Sound pressure","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":[0.0006498295,0.0006357327,0.0004644111,0.000379473,0.0001738356,0.0005570613,0.0006153825,0.0007421944,0.003154442],"category_scores_gemma":[0.003343031,0.0003148759,0.0003080736,0.000362229,0.0001802786,0.001000241,0.0004180159,0.0005644592,0.002471459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001451896,"about_ca_system_score_gemma":0.0002123688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001792095,"about_ca_topic_score_gemma":0.0003298611,"domain_scores_codex":[0.9992958,0.0001603563,0.00004339043,0.0001370837,0.0003274087,0.00003594333],"domain_scores_gemma":[0.9973081,0.001291785,0.0001840473,0.0001591021,0.000994371,0.00006255056],"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":[0.0001811437,0.00002794772,0.0008424827,0.0002162036,0.0000245446,0.000149149,0.0001095483,0.002370278,0.8359867,0.001066716,0.0005352283,0.15849],"study_design_scores_gemma":[0.00005750476,0.0009367255,0.01060356,0.00006624327,0.0001519876,0.00523505,0.000144446,0.08292615,0.8608648,0.0008766047,0.03800993,0.0001270098],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02634599,0.0008306583,0.9686744,0.0001262433,0.0001315055,0.00004764348,0.00006408801,0.0005941745,0.003185239],"genre_scores_gemma":[0.3201561,0.001856031,0.6713814,0.0003249869,0.0003188513,0.0001562459,0.0002965559,0.0002907189,0.005219237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003154442,"threshold_uncertainty_score":0.0105527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0254767897863019,"score_gpt":0.2473888458650269,"score_spread":0.2219120560787249,"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."}}