{"id":"W2045222564","doi":"10.1109/ccece.2012.6334945","title":"High dynamic range simultaneous signal compositing, applied to audio","year":2012,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Compositing; High dynamic range; Computer science; Dynamic range; Computer vision; SIGNAL (programming language); Artificial intelligence; Range (aeronautics); Sampling (signal processing); Audio signal; Audio signal processing; Digital signal processing; Computer graphics (images); Image (mathematics); Computer hardware; Engineering; Filter (signal processing)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002295514,0.0005247276,0.0002539803,0.0003175097,0.0002475017,0.0006477439,0.0003652378,0.0002945951,0.001591355],"category_scores_gemma":[0.0007652343,0.0002317633,0.0002120737,0.0004217809,0.000462643,0.000490977,0.0005204612,0.0003555413,0.0005451049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001749667,"about_ca_system_score_gemma":0.0001452286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003045828,"about_ca_topic_score_gemma":0.0005423457,"domain_scores_codex":[0.999749,0.00003948847,0.00001028866,0.00006616828,0.00011274,0.00002235767],"domain_scores_gemma":[0.9996547,0.0001576768,0.0000505005,0.00006776352,0.00004721488,0.00002219594],"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":[0.000121731,0.00003074616,0.0004205181,0.0001052921,0.00001269186,0.0001539309,0.00007904595,0.005756005,0.8583928,0.002693657,0.0002749884,0.1319586],"study_design_scores_gemma":[0.00001721367,0.0002557407,0.001698483,0.0000158593,0.00003052643,0.0008995372,0.00003712855,0.08752234,0.8985612,0.001452477,0.009487931,0.00002146748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1102958,0.001194571,0.8780906,0.0001091731,0.00006535079,0.00007840764,0.00002712806,0.0007399705,0.00939911],"genre_scores_gemma":[0.6508579,0.001069458,0.3424965,0.00009577021,0.00008162908,0.00006628712,0.0000574116,0.0001501272,0.005124946],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001591355,"threshold_uncertainty_score":0.005323589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006291283350280593,"score_gpt":0.2394465629270768,"score_spread":0.2331552795767962,"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."}}