{"id":"W2107461080","doi":"10.1155/asp.2005.25","title":"Performance of GCC- and AMDF-Based Time-Delay Estimation in Practical Reverberant Environments","year":2005,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Estimator; Reverberation; Computer science; Noise (video); Function (biology); Speech recognition; Weighting; Algorithm; Artificial intelligence; Statistics; Mathematics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0007783623,0.0001668235,0.0002368,0.0002757366,0.0001340054,0.0001361007,0.0002620647,0.000051796,0.00001457486],"category_scores_gemma":[0.00008288401,0.000144606,0.00002721645,0.000376007,0.00008396267,0.003654318,0.00004204159,0.0004669133,0.00001093645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001335058,"about_ca_system_score_gemma":0.0001425627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.355182e-7,"about_ca_topic_score_gemma":8.647897e-7,"domain_scores_codex":[0.9983296,0.00009210781,0.0005490388,0.000281134,0.0004512753,0.000296867],"domain_scores_gemma":[0.9991919,0.0001453959,0.0004347931,0.000103626,0.00003255391,0.00009169022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001172395,0.0001864514,0.00530625,0.00007360714,0.00000188292,0.00005292859,0.0002248228,0.09698179,0.006879674,0.000009562307,0.00000548094,0.8901603],"study_design_scores_gemma":[0.001319588,0.0003438131,0.003642234,0.001390233,0.000005017314,0.0002856582,0.00001938036,0.9068345,0.08477789,0.0004762264,0.0006559274,0.0002495251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5556282,0.002721457,0.4395642,0.001155793,0.00005640321,0.0001196555,4.275453e-7,0.00002286806,0.000731018],"genre_scores_gemma":[0.7970321,0.0001984438,0.2024394,0.0002566734,0.00004326259,0.000002384053,3.646803e-7,0.000008719637,0.0000187101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8899108,"threshold_uncertainty_score":0.5896863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059094959921034,"score_gpt":0.2835073755842858,"score_spread":0.2729164259850755,"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."}}