{"id":"W2165730518","doi":"10.1109/icassp.2008.4518579","title":"Perceptually based speech enhancement using the weighted &amp;#x03B2;-SA estimator","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Estimator; Weighting; Loudness; Computer science; Bayesian probability; Speech enhancement; Speech recognition; A-weighting; Set (abstract data type); Masking (illustration); Mathematics; Statistics; Artificial intelligence; Noise reduction; Acoustics","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":[0.0005041917,0.000387195,0.0003441681,0.0002044239,0.0009500191,0.0006161708,0.001980191,0.0001283612,0.00009057553],"category_scores_gemma":[0.0001428967,0.000258352,0.0001249357,0.0004166929,0.0005356674,0.0008174081,0.0002875674,0.0004860333,0.00000947469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001331251,"about_ca_system_score_gemma":0.0004931318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002709383,"about_ca_topic_score_gemma":0.000002533272,"domain_scores_codex":[0.9970391,0.00002110696,0.0006102649,0.0006460509,0.001223227,0.0004602996],"domain_scores_gemma":[0.9973829,0.00009714275,0.0006570371,0.0002176018,0.001508169,0.0001371341],"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":[0.0001363622,0.0003265778,0.001612166,0.0002513212,0.00007703591,0.00001214144,0.001939578,0.0003828644,0.9349042,0.002325864,0.0009660774,0.0570658],"study_design_scores_gemma":[0.0007754131,0.0001455382,0.0004720296,0.00104949,0.00006151943,0.0002660328,0.0005830391,0.5227152,0.4654703,0.007592528,0.0002968832,0.0005719535],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5749124,0.0001107251,0.4157623,0.002813268,0.0005817475,0.0003542962,0.00001035433,0.0001257489,0.005329181],"genre_scores_gemma":[0.8590751,0.00005462485,0.1393948,0.0007129997,0.0002572493,0.00001058271,0.000002010396,0.00002404965,0.0004685845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5223324,"threshold_uncertainty_score":0.9999869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06555149317474095,"score_gpt":0.2955163017015629,"score_spread":0.2299648085268219,"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."}}