{"id":"W4411726252","doi":"10.1109/iscas56072.2025.11043352","title":"Dynamic Spatial Filtering with Residual Spectral Mapping for Lightweight Multichannel Speech Enhancement","year":2025,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Residual; Speech enhancement; Speech recognition; Artificial intelligence; Algorithm; Noise reduction","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":[0.0001599571,0.0001786676,0.0001808137,0.0001685574,0.0002205829,0.0002645447,0.0005423387,0.00004493646,0.00002176126],"category_scores_gemma":[0.00001944368,0.0001411656,0.00004166266,0.0003135598,0.00003210993,0.0004154148,0.0001745421,0.00009205435,0.00001171315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008624355,"about_ca_system_score_gemma":0.0001239973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000378608,"about_ca_topic_score_gemma":0.000200144,"domain_scores_codex":[0.9986205,0.00001165054,0.0002298999,0.0004930996,0.0001971429,0.0004477028],"domain_scores_gemma":[0.9994165,0.00004943875,0.00006788238,0.0003272934,0.00008059978,0.00005833759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001708225,0.000319064,0.0005889549,0.0005297971,0.0002376156,0.0001001648,0.001728197,0.0003656016,0.4645762,0.007292585,0.002431059,0.5216599],"study_design_scores_gemma":[0.0008333604,0.0001394885,0.0004934252,0.0002013688,0.000008975358,0.000009656042,0.00006157406,0.0498037,0.9443636,0.002355777,0.001474955,0.0002540931],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0540657,0.00007072402,0.9389962,0.002349976,0.0004115917,0.0003095158,0.000001180808,0.0001832054,0.003611906],"genre_scores_gemma":[0.367131,0.000004749719,0.6280431,0.0003589804,0.00006849031,0.00003751459,0.000003917531,0.000007979804,0.004344372],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5214058,"threshold_uncertainty_score":0.5756568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009850163070860217,"score_gpt":0.2466236882901707,"score_spread":0.2367735252193105,"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."}}