{"id":"W4385689440","doi":"10.1007/978-3-031-36974-2_9","title":"Binaural Beamforming","year":2023,"lang":"en","type":"book-chapter","venue":"Springer topics in signal processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Binaural recording; Beamforming; Monaural; Speech recognition; Computer science; Intelligibility (philosophy); Acoustics; Telecommunications; Physics; Philosophy","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":[0.0002000835,0.00138378,0.0005969677,0.0008887678,0.0004428253,0.001553613,0.0007221238,0.001049912,0.1196043],"category_scores_gemma":[0.0004462524,0.0004524789,0.0003764878,0.001302393,0.0003766506,0.00117437,0.001317162,0.001110936,0.1007653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003283688,"about_ca_system_score_gemma":0.000345907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003562691,"about_ca_topic_score_gemma":0.0008524457,"domain_scores_codex":[0.9998056,0.00001797006,0.000008416375,0.00004495453,0.0001060727,0.00001690438],"domain_scores_gemma":[0.9998404,0.0000347299,0.000008465096,0.00003719251,0.00006282682,0.00001633848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008050335,0.00002387285,0.00009195473,0.0002551831,0.00002657668,0.00009294785,0.00005132196,0.002991234,0.066737,0.03581985,0.06275641,0.8310732],"study_design_scores_gemma":[0.00002141705,0.00009474703,0.0009246493,0.0002236079,0.00005013206,0.001810407,0.00009159713,0.01643997,0.05461526,0.04176223,0.8838955,0.0000705125],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.001162523,0.009245057,0.6918634,0.0004510431,0.00167742,0.00006099456,0.0004995111,0.004489495,0.2905506],"genre_scores_gemma":[0.0266546,0.01229262,0.1753043,0.0007783278,0.0007037603,0.0001166906,0.001413696,0.001332438,0.7814037],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1196043,"threshold_uncertainty_score":0.4001162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03389838589381251,"score_gpt":0.2592472347005534,"score_spread":0.2253488488067409,"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."}}