{"id":"W2124804097","doi":"10.1109/icif.2003.177473","title":"Adaptive time-frequency data fusion for speech enhancement","year":2003,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Speech recognition; Speech enhancement; Signal-to-noise ratio (imaging); Noise (video); Noise reduction; Gaussian noise; SIGNAL (programming language); Filter (signal processing); Reduction (mathematics); Algorithm; Degradation (telecommunications); Artificial intelligence; Telecommunications; Mathematics; Computer vision","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":[0.0003514168,0.00009673057,0.00009699848,0.00003793981,0.0001279575,0.0001101655,0.0009927723,0.00003132482,0.0001917737],"category_scores_gemma":[0.00008118572,0.00007906224,0.00002258161,0.0001898936,0.00001823281,0.0008757897,0.0002195059,0.00004386697,0.0002132568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002635925,"about_ca_system_score_gemma":0.0001107797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000640174,"about_ca_topic_score_gemma":0.000005497223,"domain_scores_codex":[0.9989584,0.00001856536,0.0001435567,0.0004459299,0.0001803647,0.0002531484],"domain_scores_gemma":[0.9989756,0.00003911129,0.00005429977,0.0007920998,0.00007527353,0.00006359432],"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":[0.000009961495,0.0002172411,0.0000698893,0.00002703285,0.00003016654,0.00001449219,0.0001787074,0.000004316043,0.1455661,0.04176691,0.04618031,0.7659349],"study_design_scores_gemma":[0.0004204934,0.0001748047,0.00001267407,0.00002626969,0.000006117324,0.00001457915,0.00002131836,0.01225679,0.9243976,0.03188573,0.0305387,0.0002448994],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000990078,0.0001614208,0.9603328,0.0004601417,0.0002101536,0.000194486,0.000003712654,0.00009212529,0.03755514],"genre_scores_gemma":[0.02006514,0.000009956146,0.9739124,0.000544549,0.00004829407,0.00001088115,0.00001575252,0.000006186605,0.005386821],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7788315,"threshold_uncertainty_score":0.3224065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04817572613710732,"score_gpt":0.2834592252356254,"score_spread":0.2352834990985181,"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."}}