{"id":"W2061284695","doi":"10.1007/s10772-013-9189-1","title":"Computational auditory models in predicting noise reduction performance for wideband telephony applications","year":2013,"lang":"en","type":"article","venue":"International Journal of Speech Technology","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Speech recognition; Noise (video); Wideband; Noise reduction; Reliability (semiconductor); Wideband audio; Reduction (mathematics); Telephony; Quality (philosophy); Artificial intelligence; Speech coding; Telecommunications; Mathematics","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":[0.0002680656,0.0001019249,0.0001553656,0.0008366195,0.0000742166,0.0001080664,0.00105641,0.0001107124,0.000008618798],"category_scores_gemma":[0.00006741563,0.00009828065,0.00005599725,0.0003684607,0.00007517279,0.001453207,0.0001066226,0.0002716154,0.00001082863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001681935,"about_ca_system_score_gemma":0.0001631322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007293851,"about_ca_topic_score_gemma":0.000001555451,"domain_scores_codex":[0.9987277,0.00001006172,0.0005319019,0.0001953624,0.0003497256,0.0001852395],"domain_scores_gemma":[0.9982734,0.00006933574,0.0004397698,0.0001315721,0.001042559,0.00004333982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002940483,0.0001533926,0.005096209,0.00002115866,0.00007262567,0.00001245615,0.0002321196,0.03827256,0.02508502,0.003114206,0.001246985,0.9266639],"study_design_scores_gemma":[0.00260932,0.0003420718,0.004104342,0.0003299775,0.00001439374,0.002741729,0.0003598219,0.3225617,0.1705374,0.4917199,0.004282964,0.0003963289],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3467234,0.000149968,0.6461827,0.005808624,0.0006763263,0.0002163518,0.000001829669,0.00006470294,0.0001760612],"genre_scores_gemma":[0.7435564,0.00003962876,0.2558143,0.00007046564,0.0004121572,0.00005690014,0.000002119337,0.000006812242,0.00004132046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9262675,"threshold_uncertainty_score":0.4007769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009768127122746243,"score_gpt":0.2514530187062966,"score_spread":0.2416848915835504,"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."}}