{"id":"W4408354526","doi":"10.1109/icassp49660.2025.10888043","title":"Microphone Array Beamforming for Speech Enhancement Based on Dynamic Mode Decomposition","year":2025,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Research and Development","keywords":"Speech enhancement; Beamforming; Microphone array; Computer science; Dynamic mode decomposition; Speech recognition; Acoustics; Mode (computer interface); Noise-canceling microphone; Microphone; Noise reduction; Telecommunications; Physics; Artificial intelligence; Human–computer interaction","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":[],"consensus_categories":[],"category_scores_codex":[0.0001331514,0.0001160041,0.000115488,0.0001517211,0.0001866748,0.0001445588,0.0003550367,0.00003832559,0.00001198763],"category_scores_gemma":[0.00001123382,0.0001041928,0.00006080255,0.0002687543,0.00001207426,0.0001921319,0.00003575266,0.00005844208,0.00001898772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001242847,"about_ca_system_score_gemma":0.0001059394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008998678,"about_ca_topic_score_gemma":0.00001352164,"domain_scores_codex":[0.9991059,0.000009734263,0.0001725256,0.0003297035,0.0001323933,0.0002497775],"domain_scores_gemma":[0.9995025,0.00006783474,0.00004988819,0.0002823386,0.00005953425,0.00003795041],"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":[0.00002062796,0.00008429959,0.000008921074,0.00003989035,0.00000646983,9.752749e-7,0.00002746235,0.0009517233,0.8818699,0.000313823,0.0003090076,0.1163669],"study_design_scores_gemma":[0.0003673561,0.00006887307,0.000009401648,0.00008667068,0.000003437548,8.138047e-7,0.000006374527,0.209877,0.7864083,0.002432678,0.0006419315,0.00009711606],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.014219,0.00002881278,0.9776278,0.001615834,0.0003174168,0.0002346317,0.000001514093,0.0001136713,0.005841372],"genre_scores_gemma":[0.25501,0.00000228504,0.7418069,0.002185871,0.00001499068,0.00003154969,0.00000797442,0.000004379013,0.000936125],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.240791,"threshold_uncertainty_score":0.4248857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006492301562476007,"score_gpt":0.3089318436487502,"score_spread":0.3024395420862742,"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."}}