{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001484063,0.000975403,0.0006783782,0.001151475,0.000273313,0.0005564661,0.0007485449,0.00104594,0.0009351649],"category_scores_gemma":[0.007528203,0.0002413388,0.0003879893,0.0006949937,0.0003482547,0.0008220737,0.0006747111,0.0005364504,0.0004374159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003702217,"about_ca_system_score_gemma":0.0008743478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007233962,"about_ca_topic_score_gemma":0.00519934,"domain_scores_codex":[0.9991942,0.0001752227,0.00006051962,0.0001812737,0.0002980332,0.00009083089],"domain_scores_gemma":[0.9970167,0.00133115,0.0002851735,0.0002890244,0.0009660895,0.0001119969],"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":[0.002240977,0.0002033388,0.025202,0.0003362519,0.0003193153,0.0004532827,0.0003522774,0.2341499,0.1153052,0.002212597,0.001965308,0.6172596],"study_design_scores_gemma":[0.00006242262,0.0002586149,0.01364398,0.00003672426,0.00008233351,0.0005866694,0.0000668234,0.9143439,0.0685879,0.0004398959,0.001785731,0.0001050102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4892827,0.002740736,0.5012969,0.0002207739,0.0002319191,0.00004964678,0.0002676166,0.00334545,0.002564253],"genre_scores_gemma":[0.7934006,0.0004801692,0.2039246,0.0001103188,0.00005926889,0.00003015164,0.0005535335,0.0001212018,0.001320128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007233962,"threshold_uncertainty_score":0.01438367,"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."}}