{"id":"W4317383774","doi":"10.1109/robio55434.2022.10012011","title":"Acoustic Beamforming via Interference-Plus-Noise Covariance Matrix Construction for Interferences and Noise Attenuation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Beamforming; Covariance matrix; Noise (video); Acoustics; Interference (communication); Computer science; Microphone array; Attenuation; Microphone; Noise measurement; Robustness (evolution); Covariance; Algorithm; Mathematics; Speech recognition; Noise reduction; Telecommunications; Sound pressure; Physics; Artificial intelligence; Statistics; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004201184,0.0002721026,0.0002756327,0.0003463716,0.0005148399,0.0005684403,0.0007391386,0.00007768715,0.00007967511],"category_scores_gemma":[0.00006857083,0.0002702699,0.00006577844,0.0002758527,0.0001841388,0.0003705941,0.0004269961,0.000297977,0.000005775095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001578434,"about_ca_system_score_gemma":0.0001624122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001677664,"about_ca_topic_score_gemma":0.00001388859,"domain_scores_codex":[0.99812,0.00006155155,0.0004543734,0.0006521271,0.0003992614,0.0003126948],"domain_scores_gemma":[0.9988239,0.0001646254,0.0003431044,0.0002431231,0.0003059201,0.000119315],"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":[0.0006530229,0.0004586739,0.00143456,0.0002706708,0.0003828355,0.00003273699,0.002034117,0.047708,0.4844202,0.2311641,0.001039606,0.2304015],"study_design_scores_gemma":[0.001312646,0.000760506,0.0001206809,0.0001408195,0.00005463045,0.0001799942,0.0005534704,0.9412079,0.0237716,0.03103501,0.0003134893,0.0005492502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02800806,0.000138477,0.965275,0.003239295,0.00246659,0.0003575296,0.0001008846,0.0000800152,0.0003340833],"genre_scores_gemma":[0.8963769,0.0001673795,0.1025019,0.0003140839,0.0001218377,0.00006963464,0.00005843338,0.00001549674,0.0003743141],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8934999,"threshold_uncertainty_score":0.999975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03730590665231713,"score_gpt":0.2923948505461037,"score_spread":0.2550889438937866,"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."}}