{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":16,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":16,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"a8a6aac901d9","filters":{"venue":"IEEE Transactions on Speech and Audio Processing"}},"results":[{"id":"W2121415728","doi":"10.1109/tsa.2004.840940","title":"Eigenvoice modeling with sparse training data","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":472,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Speech recognition; Training set; Set (abstract data type); Adaptation (eye); Limit (mathematics); Training (meteorology); Maximum likelihood; Covariance matrix; Pattern recognition (psychology); Estimation theory; Artificial intelligence; Covariance; Speaker recognition; Algorithm; Mathematics; Statistics","authors":[{"name":"Patrick Kenny","is_ca":true},{"name":"Gilles Boulianne","is_ca":true},{"name":"Pierre Dumouchel","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.095794255220844,"gpt":0.2797844726229301,"spread":0.1839902174020861,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007939901,0.0005304951,0.0007143595,0.0004945954,0.0002693826,0.0007678655,0.001352672,0.0008075425,0.001485285],"category_scores_gemma":[0.003208085,0.0005329204,0.0006735871,0.0005643721,0.0005621274,0.001225997,0.001061094,0.001267448,0.000690662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000251907,"about_ca_system_score_gemma":0.0004872123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00244556,"about_ca_topic_score_gemma":0.004236388,"domain_scores_codex":[0.9995765,0.0001189921,0.00001632579,0.000101235,0.0001287723,0.00005818656],"domain_scores_gemma":[0.9991251,0.0004424506,0.00008881113,0.0001513066,0.0001627444,0.00002962728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001051985,0.00005204139,0.000860624,0.00009482021,0.0000609196,0.0001085586,0.000232753,0.8014677,0.01244755,0.03343184,0.002237855,0.1489002],"study_design_scores_gemma":[0.000002632565,0.000007831561,0.0001053101,0.000003516658,0.000002966722,0.00002115068,0.000006456563,0.9912905,0.001047843,0.006963778,0.0005419836,0.000006088376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005680046,0.00006269001,0.9935176,0.00004362754,0.00001368886,0.000009312174,0.00004555209,0.0001437738,0.0004837273],"genre_scores_gemma":[0.4754205,0.0005498425,0.515599,0.0001857664,0.0001511257,0.0002279883,0.0007995635,0.0002749555,0.006791252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00244556,"threshold_uncertainty_score":0.004968762,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2135817141","doi":"10.1109/tsa.2004.833008","title":"Time-Delay Estimation via Linear Interpolation and Cross Correlation","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":243,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Reverberation; Multilateration; Cross-correlation; Computer science; Microphone; Multipath propagation; Algorithm; Noise (video); Interpolation (computer graphics); SIGNAL (programming language); Linear interpolation; Speech recognition; Acoustics; Mathematics; Artificial intelligence; Telecommunications; Pattern recognition (psychology)","authors":[{"name":"Jacob Benesty","is_ca":true},{"name":"Jun Chen","is_ca":false},{"name":"Yuli Huang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00977493263216831,"gpt":0.2592749246945659,"spread":0.2494999920623976,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009391968,0.0008023282,0.0005341196,0.001130531,0.0003224172,0.0006962012,0.0008653183,0.000630002,0.001735595],"category_scores_gemma":[0.003552416,0.0003548874,0.000633362,0.00146056,0.0003904032,0.0009381155,0.0009964828,0.001123117,0.0007820987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004401126,"about_ca_system_score_gemma":0.00114351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00558165,"about_ca_topic_score_gemma":0.004875625,"domain_scores_codex":[0.9994602,0.0001141125,0.0000276613,0.0001320555,0.000216607,0.00004945629],"domain_scores_gemma":[0.9987283,0.0005850436,0.0001531469,0.0001487839,0.0003497699,0.00003495176],"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":[0.0002135164,0.00009086967,0.002076574,0.0001676528,0.0001039396,0.0001964845,0.0001478526,0.4632329,0.02667249,0.01666992,0.002121609,0.4883062],"study_design_scores_gemma":[0.000005577347,0.00003318065,0.0003847108,0.000008234362,0.00001041224,0.00008355149,0.000009689393,0.9904149,0.006056963,0.001402814,0.001573908,0.00001605376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006660045,0.0001741338,0.9922536,0.00002546041,0.00002278875,0.00001129145,0.00002161797,0.0003429293,0.0004881139],"genre_scores_gemma":[0.1944244,0.0005513509,0.8008049,0.00005108392,0.00006615481,0.00007365204,0.0002939159,0.0001835803,0.003551073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00558165,"threshold_uncertainty_score":0.01109833,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2000916836","doi":"10.1109/89.902276","title":"An adaptive KLT approach for speech enhancement","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":235,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Speech enhancement; Speech recognition; Noise (video); Computer science; Speech processing; White noise; Additive white Gaussian noise; Residual; Background noise; Colors of noise; Distortion (music); Mathematics; Artificial intelligence; Noise reduction; Algorithm; Telecommunications","authors":[{"name":"A. Rezayee","is_ca":true},{"name":"Saeed Gazor","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0278643172740081,"gpt":0.2735090793971463,"spread":0.2456447621231382,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004629183,0.0007296977,0.0005198315,0.0007440879,0.0002657289,0.0005898561,0.0006896963,0.0006557532,0.002078975],"category_scores_gemma":[0.0009670551,0.0002483186,0.0006885913,0.0005719202,0.0004379277,0.0008595167,0.0006135628,0.0007517397,0.001308603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003030722,"about_ca_system_score_gemma":0.0004103679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008711552,"about_ca_topic_score_gemma":0.001368324,"domain_scores_codex":[0.9995084,0.00007657913,0.00002993704,0.0001027159,0.0002499982,0.00003239295],"domain_scores_gemma":[0.9996955,0.00009743718,0.00002743288,0.00003528197,0.0001320033,0.00001228741],"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":[0.000199089,0.00007506512,0.0004037103,0.0001660612,0.00006520696,0.0001566409,0.0001320803,0.05082987,0.2024493,0.01043239,0.001615354,0.7334752],"study_design_scores_gemma":[0.00002840018,0.0002158452,0.0008845259,0.00001991648,0.00005633854,0.0006063598,0.00004327236,0.8977051,0.08075202,0.005864569,0.01377593,0.00004776431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001977151,0.0001975784,0.9968891,0.00003264851,0.0000251283,0.0000152254,0.00000878919,0.0002576773,0.0005967661],"genre_scores_gemma":[0.08430077,0.0006371111,0.9094498,0.0001243682,0.00008878113,0.00007807832,0.0001226441,0.0001249397,0.005073483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002078975,"threshold_uncertainty_score":0.006954908,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2128604088","doi":"10.1109/tsa.2003.818031","title":"Incorporating the human hearing properties in the signal subspace approach for speech enhancement","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":171,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Speech recognition; Speech enhancement; Computer science; Signal subspace; Noise (video); Spectrogram; Noise reduction; Residual; Colors of noise; Subspace topology; Filter (signal processing); Artificial intelligence; Algorithm; Computer vision","authors":[{"name":"F. Jabloun","is_ca":true},{"name":"Benoı̂t Champagne","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04549423966659918,"gpt":0.264615521974624,"spread":0.2191212823080248,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003631875,0.0005648311,0.0003603335,0.0003027857,0.0001871996,0.0004266967,0.0002441204,0.0005556873,0.001995727],"category_scores_gemma":[0.0006798014,0.0002013846,0.0005405328,0.0002724043,0.0003328492,0.0008342257,0.0003276009,0.0004108854,0.001025173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008463814,"about_ca_system_score_gemma":0.0002652854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003972455,"about_ca_topic_score_gemma":0.0008388362,"domain_scores_codex":[0.9998577,0.00005346571,0.000008450925,0.00001854322,0.00005344889,0.000008488321],"domain_scores_gemma":[0.9998042,0.0001016985,0.00001157122,0.00003311524,0.00004353614,0.000005989078],"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":[0.0001994317,0.0000951621,0.0003680762,0.000258198,0.00006016653,0.000280279,0.0001586173,0.08580533,0.4519422,0.01788746,0.000867618,0.4420775],"study_design_scores_gemma":[0.00002614269,0.0006423569,0.001013364,0.00003118947,0.00006381241,0.001487559,0.00007736367,0.7922491,0.1775798,0.01217186,0.01458217,0.00007534271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005253729,0.0001625012,0.99368,0.00003809961,0.00001997902,0.00001430388,0.000009518229,0.0002532347,0.0005686007],"genre_scores_gemma":[0.1443982,0.001096294,0.8510427,0.000073806,0.00006021561,0.00005615828,0.00009345928,0.0000910809,0.003087987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001995727,"threshold_uncertainty_score":0.006676316,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2100555417","doi":"10.1109/tsa.2003.815518","title":"A soft voice activity detector based on a laplacian-gaussian model","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":141,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Speech recognition; Hidden Markov model; Computer science; Noise (video); Discrete cosine transform; Gaussian; Posterior probability; Detector; Probability distribution; Bayesian probability; Pattern recognition (psychology); Mathematics; Artificial intelligence; Statistics","authors":[{"name":"Saeed Gazor","is_ca":true},{"name":"Wei Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01821155687566045,"gpt":0.2488104677728221,"spread":0.2305989108971617,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009163311,0.0005790011,0.001027905,0.0009435171,0.0002643696,0.001004967,0.001150841,0.001006141,0.0015051],"category_scores_gemma":[0.002366951,0.0003603991,0.0006736009,0.0005583711,0.0005754491,0.001355773,0.00083384,0.0008663945,0.0008030797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005112033,"about_ca_system_score_gemma":0.0005900709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001840861,"about_ca_topic_score_gemma":0.001980227,"domain_scores_codex":[0.9992642,0.0001653434,0.00003241333,0.0001784873,0.0002925458,0.00006699112],"domain_scores_gemma":[0.9990442,0.0005858412,0.00005844174,0.00006868667,0.0001946571,0.00004809914],"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":[0.0005737302,0.0001882091,0.004332872,0.0002496879,0.0002072849,0.0003746977,0.0001654588,0.1957909,0.07481424,0.02419837,0.003757159,0.6953474],"study_design_scores_gemma":[0.00001312759,0.00005526627,0.0005838284,0.000005697992,0.00001733539,0.000168727,0.000009189828,0.989854,0.005208374,0.003042235,0.001018975,0.00002322362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006184169,0.0002132505,0.992373,0.00006513816,0.00004330728,0.0000220412,0.00003643591,0.0004466902,0.0006159724],"genre_scores_gemma":[0.5598485,0.0006526265,0.4331001,0.0004332405,0.0001583067,0.0001460891,0.0003631708,0.0001065881,0.005191373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001840861,"threshold_uncertainty_score":0.005035102,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2096441936","doi":"10.1109/tsa.2005.851925","title":"A frequency domain method for blind source separation of convolutive audio mixtures","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Blind signal separation; Algorithm; Initialization; Frequency domain; Computer science; Mixing (physics); Joint (building); Bin; Speech recognition; Permutation (music); Spectral density; Mathematics; Acoustics","authors":[{"name":"Kambiz Rahbar","is_ca":true},{"name":"J.P. Reilly","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01936232405151699,"gpt":0.3218353127592473,"spread":0.3024729887077303,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005773951,0.0008618149,0.0006171268,0.0009111896,0.0004407159,0.0005406355,0.0007161337,0.000969068,0.002723134],"category_scores_gemma":[0.001195952,0.0003341518,0.0006145428,0.000648643,0.0006453905,0.001029612,0.0006566234,0.001214135,0.00230277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003022135,"about_ca_system_score_gemma":0.0005980161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007705868,"about_ca_topic_score_gemma":0.001076245,"domain_scores_codex":[0.9994382,0.0001107398,0.0000274311,0.000110966,0.0002923028,0.00002034771],"domain_scores_gemma":[0.9996153,0.0001273078,0.00003152435,0.00005707391,0.0001505423,0.00001823256],"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":[0.0001957178,0.00009942663,0.0002579902,0.0003458516,0.0001030633,0.0001152355,0.00008983287,0.0573147,0.1341763,0.03448757,0.003721916,0.7690926],"study_design_scores_gemma":[0.00007337402,0.0002135305,0.000645768,0.00003747438,0.00005972961,0.001036179,0.0000354355,0.8668178,0.06585313,0.01525385,0.04986159,0.0001121332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005050144,0.0001476398,0.9988146,0.00002389786,0.00004818917,0.00001130851,0.00001039442,0.0001249639,0.0003141197],"genre_scores_gemma":[0.01379418,0.0003490565,0.9834322,0.0000529528,0.00009109887,0.00006768737,0.00007645104,0.00004566506,0.002090649],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002723134,"threshold_uncertainty_score":0.009109795,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2137996830","doi":"10.1109/tsa.2005.851941","title":"A blind channel identification-based two-stage approach to separation and dereverberation of speech signals in a reverberant environment","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec","funders":"","keywords":"Reverberation; Blind signal separation; MIMO; Robustness (evolution); Computer science; Speech recognition; Source separation; Interference (communication); Channel (broadcasting); Signal processing; Acoustics; Algorithm; Telecommunications; Physics","authors":[{"name":"Yiteng Huang","is_ca":false},{"name":"Jacob Benesty","is_ca":true},{"name":"Jindong Chen","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02512904293596091,"gpt":0.2882898073889731,"spread":0.2631607644530122,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002958271,0.0005657201,0.0005435385,0.0004156168,0.0003957626,0.0004241731,0.0005578955,0.0007069929,0.001609182],"category_scores_gemma":[0.0007435763,0.00030474,0.0005218984,0.0003098584,0.0006416197,0.0006680832,0.0005340055,0.0006712755,0.000717083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003769696,"about_ca_system_score_gemma":0.0007930548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001654529,"about_ca_topic_score_gemma":0.002831252,"domain_scores_codex":[0.9997028,0.00006654617,0.00001846057,0.00008794169,0.0000909731,0.00003331594],"domain_scores_gemma":[0.999743,0.00008651624,0.00002507783,0.00004443182,0.00008535814,0.00001558779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005959478,0.0001867705,0.0007056157,0.0002558084,0.00009583619,0.0002397959,0.0002639554,0.2888205,0.1964595,0.03414405,0.001853222,0.476379],"study_design_scores_gemma":[0.0000292712,0.0001594557,0.0004567623,0.000006288288,0.00002037512,0.0002209684,0.00002004195,0.9517013,0.03871097,0.006134022,0.00250227,0.00003829616],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004850439,0.00008336382,0.9941993,0.00002715657,0.0000193814,0.00001722572,0.00001282961,0.000200896,0.0005895158],"genre_scores_gemma":[0.2079351,0.0002954559,0.7855135,0.00007487712,0.00005591882,0.0001001659,0.00009073473,0.00004262917,0.005891663],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001654529,"threshold_uncertainty_score":0.005383253,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2145103350","doi":"10.1109/89.966081","title":"Linear prediction based packet loss concealment algorithm for PCM coded speech","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Nortel (Canada)","funders":"","keywords":"Linear prediction; Computer science; Speech coding; Algorithm; Packet loss; Speech recognition; Network packet; Voice activity detection; Linear predictive coding; Frame (networking); Pulse-code modulation; Speech processing; SIGNAL (programming language); Speech enhancement; Residual; Artificial intelligence; Noise reduction; Telecommunications; Computer network","authors":[{"name":"E. Gunduzhan","is_ca":true},{"name":"K. Momtahan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02324473546787878,"gpt":0.2909834385252361,"spread":0.2677387030573573,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009189594,0.0004718385,0.0004139044,0.0005587091,0.0003766887,0.0005698758,0.000785697,0.0006282299,0.001397443],"category_scores_gemma":[0.002094222,0.0002059544,0.0002172008,0.000477722,0.0003353911,0.000902647,0.000481672,0.0009063433,0.000801254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005255635,"about_ca_system_score_gemma":0.0008097667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002379912,"about_ca_topic_score_gemma":0.002331871,"domain_scores_codex":[0.9995735,0.00007096869,0.00003095506,0.00006464386,0.0002278726,0.0000320691],"domain_scores_gemma":[0.99911,0.000333417,0.0001153156,0.00009789895,0.0003195776,0.00002378861],"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":[0.0006244583,0.00008891188,0.0007267376,0.00008285735,0.00003097485,0.00008495649,0.0001329285,0.08902843,0.04818764,0.004955851,0.00186603,0.8541902],"study_design_scores_gemma":[0.00003889014,0.0001214085,0.0004605179,0.000009576795,0.00001772563,0.0001240877,0.00001705538,0.9516931,0.04447029,0.001026159,0.002004626,0.00001662051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01016222,0.0002405828,0.9883683,0.00005682003,0.00002842011,0.00003168899,0.00001686097,0.0007654197,0.0003297556],"genre_scores_gemma":[0.1823283,0.000391269,0.8131051,0.00007793192,0.00004216427,0.0001085436,0.0001329722,0.00006573882,0.00374789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002379912,"threshold_uncertainty_score":0.004859984,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2101999036","doi":"10.1109/tsa.2005.851943","title":"Speech enhancement employing Laplacian-Gaussian mixture","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Speech recognition; Wiener filter; Computer science; Estimator; Minimum mean square error; Speech enhancement; Gaussian; Computational complexity theory; Filter (signal processing); Noise (video); Algorithm; Mathematics; Artificial intelligence; Statistics; Physics","authors":[{"name":"Saeed Gazor","is_ca":true},{"name":"Wei Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01368805715212479,"gpt":0.2546881058707494,"spread":0.2410000487186246,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004486384,0.000472141,0.0004861124,0.0005324307,0.0001817481,0.0003897675,0.0004357215,0.0005103112,0.0009385496],"category_scores_gemma":[0.0008865066,0.0002326755,0.0005757287,0.0004323955,0.0002812299,0.0008246208,0.0006649139,0.0004259997,0.0006423585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002193159,"about_ca_system_score_gemma":0.000297044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001030732,"about_ca_topic_score_gemma":0.001817998,"domain_scores_codex":[0.9997763,0.00004366728,0.0000106236,0.00003723846,0.0001162434,0.00001604136],"domain_scores_gemma":[0.9997845,0.00009775748,0.00001750645,0.00002521047,0.00006545375,0.000009584506],"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":[0.0002810904,0.00008452674,0.001127435,0.000158582,0.0001252842,0.0002030517,0.0001410811,0.1568115,0.1599396,0.01447048,0.00257375,0.6640836],"study_design_scores_gemma":[0.00001709266,0.0000664453,0.0005033106,0.000006819057,0.00003242649,0.0002224235,0.00001250036,0.9634649,0.02854105,0.002428499,0.004680494,0.00002402839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006099049,0.0002259699,0.9925572,0.00003886534,0.00002072694,0.000008893188,0.000007794783,0.0003530012,0.0006884689],"genre_scores_gemma":[0.1927454,0.0006515088,0.801766,0.0001313836,0.00006087658,0.00003249533,0.0001209022,0.00009163809,0.004399719],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001030732,"threshold_uncertainty_score":0.003139794,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2133278758","doi":"10.1109/tsa.2004.825668","title":"Speaker Adaptation Using an Eigenphone Basis","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Speech recognition; Adaptation (eye); Computer science; Basis (linear algebra); Mathematics; Psychology","authors":[{"name":"Patrick Kenny","is_ca":true},{"name":"Gilles Boulianne","is_ca":true},{"name":"Pierre Ouellet","is_ca":true},{"name":"Pierre Dumouchel","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03702613246256788,"gpt":0.2671019762390837,"spread":0.2300758437765159,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008481022,0.0006041368,0.0007883361,0.0007241071,0.0004352703,0.0006917498,0.001056678,0.0009525724,0.003623582],"category_scores_gemma":[0.002240569,0.0005367433,0.001082074,0.0007554524,0.0003566417,0.001314752,0.001129973,0.001702683,0.003063248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002489262,"about_ca_system_score_gemma":0.0005750708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001999607,"about_ca_topic_score_gemma":0.003886687,"domain_scores_codex":[0.9990813,0.0002626979,0.00003265952,0.0002169503,0.0003451435,0.00006132636],"domain_scores_gemma":[0.9993702,0.0002292457,0.00003767497,0.000148595,0.0001846278,0.00002970829],"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":[0.0001836099,0.0001400838,0.001233666,0.0001065192,0.0001926804,0.0001309288,0.0002226045,0.1015619,0.05580735,0.02699632,0.004553371,0.8088709],"study_design_scores_gemma":[0.000009913311,0.00003992098,0.0008711485,0.00001257792,0.00003226868,0.0001629297,0.00002206009,0.9701822,0.01069102,0.01169685,0.006240435,0.00003865693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00298726,0.0001330988,0.9956145,0.00004526156,0.00006593317,0.00001577109,0.00003695712,0.0004411596,0.0006600612],"genre_scores_gemma":[0.09107227,0.0004568399,0.9001439,0.0001858421,0.0001657333,0.00015046,0.0004242467,0.0003609159,0.00703974],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003623582,"threshold_uncertainty_score":0.01212209,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2136836257","doi":"10.1109/89.979381","title":"A robust compensation strategy for extraneous acoustic variations in spontaneous speech recognition","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Hidden Markov model; Speech recognition; Computer science; Word error rate; Pattern recognition (psychology); Pronunciation; Bayesian probability; Artificial intelligence","authors":[{"name":"Hui Jiang","is_ca":true},{"name":"Li Deng","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07417596438202625,"gpt":0.2557498206924044,"spread":0.1815738563103781,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006281213,0.0007484098,0.0006161236,0.0002981406,0.0002113053,0.0003997089,0.0009519604,0.0006276482,0.0009952143],"category_scores_gemma":[0.002017088,0.000285914,0.0003636464,0.0002605884,0.000331465,0.0006888742,0.0005327769,0.0005069792,0.0008416689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001532833,"about_ca_system_score_gemma":0.0004374111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001015645,"about_ca_topic_score_gemma":0.001348171,"domain_scores_codex":[0.9993579,0.0001285288,0.0000466912,0.0001681458,0.0002495802,0.00004918718],"domain_scores_gemma":[0.999342,0.0001654252,0.00009221728,0.000172363,0.0001941003,0.00003385319],"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":[0.0003005922,0.0001576346,0.0009156337,0.0001176882,0.00008503897,0.0002106334,0.0001216256,0.03074363,0.3855463,0.002394442,0.001421596,0.5779852],"study_design_scores_gemma":[0.00005522085,0.0004662129,0.004329623,0.00001467254,0.00008455502,0.001112839,0.00004906857,0.7590358,0.2274146,0.002139,0.005210404,0.00008802263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02829794,0.000222077,0.9698738,0.00005421996,0.00004176222,0.00003644418,0.00004698781,0.001021631,0.0004052262],"genre_scores_gemma":[0.4721231,0.0002943829,0.5236089,0.0001648824,0.00008954896,0.0002146589,0.0004884982,0.0001948592,0.002821241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001015645,"threshold_uncertainty_score":0.003329337,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2135433537","doi":"10.1109/89.928919","title":"A maximum a posteriori approach to speaker adaptation using the trended hidden Markov model","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Hidden Markov model; Maximum a posteriori estimation; Polynomial; Computer science; Speech recognition; Adaptation (eye); Security token; A priori and a posteriori; Gaussian; Pattern recognition (psychology); Mathematics; Algorithm; Artificial intelligence; Statistics; Maximum likelihood","authors":[{"name":"Rathinavelu Chengalvarayan","is_ca":true},{"name":"Li Deng","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05320708971129079,"gpt":0.2664378542114752,"spread":0.2132307645001844,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001832936,0.0008252242,0.0007835683,0.0006302447,0.0004692294,0.0006953855,0.001194837,0.0009646951,0.001778853],"category_scores_gemma":[0.005309707,0.0007137236,0.001184525,0.0006449051,0.0004853203,0.001082699,0.0009029773,0.001818321,0.001095833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003985997,"about_ca_system_score_gemma":0.0009808929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003462002,"about_ca_topic_score_gemma":0.004111886,"domain_scores_codex":[0.9991116,0.000374558,0.00004366664,0.0002139078,0.000210363,0.00004584764],"domain_scores_gemma":[0.998549,0.0009732594,0.0000692964,0.0001475515,0.0002388204,0.00002210935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002645403,0.00008555743,0.0008579001,0.0001923752,0.000283573,0.0001703096,0.0002881763,0.5486665,0.02383363,0.01949948,0.002393232,0.4034648],"study_design_scores_gemma":[0.000007903114,0.00003963052,0.000298491,0.000008579917,0.00002546788,0.00005006128,0.00001284629,0.986062,0.003299021,0.008931574,0.001244543,0.00001992438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00179525,0.00007611202,0.9975604,0.00003240562,0.00001241939,0.00001362107,0.00002227306,0.0002464506,0.000241023],"genre_scores_gemma":[0.1215744,0.0004451406,0.8738481,0.00008404018,0.0001123393,0.0002177667,0.0003144291,0.0002667887,0.003137077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003462002,"threshold_uncertainty_score":0.009693563,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2170959418","doi":"10.1109/tsa.2003.814411","title":"Quantization of lsf parameters using a trellis modeling","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Trellis quantization; Vector quantization; Algorithm; Quantization (signal processing); Speech recognition; Mathematics; Computer science; Trellis (graph); Speech coding; Decoding methods; Artificial intelligence; Image processing","authors":[{"name":"Farshad Lahouti","is_ca":true},{"name":"Amir K. Khandani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04364163151637969,"gpt":0.2915569645516571,"spread":0.2479153330352774,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007036287,0.0005230044,0.000488507,0.0005304921,0.0003475393,0.0007858685,0.000771919,0.0006105533,0.002648687],"category_scores_gemma":[0.002020814,0.000306357,0.0005949419,0.0007198848,0.0004647375,0.001086929,0.000285506,0.0009340076,0.001042499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009451137,"about_ca_system_score_gemma":0.0009916887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004621288,"about_ca_topic_score_gemma":0.005157181,"domain_scores_codex":[0.9993748,0.0001570794,0.00004921508,0.00008748638,0.0002832813,0.00004809166],"domain_scores_gemma":[0.9991886,0.0003344515,0.0000954241,0.0001214453,0.0002442992,0.00001573568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001764065,0.00004998904,0.000412565,0.0001497221,0.00004483091,0.0001033866,0.0001110737,0.714528,0.06587768,0.05966205,0.002375631,0.1565087],"study_design_scores_gemma":[0.000008463563,0.00005488248,0.00009123177,0.00001093651,0.000008615039,0.00005265874,0.00000656242,0.980239,0.01336478,0.003892994,0.002254442,0.0000154642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003657015,0.000178217,0.9947618,0.0000502783,0.00002379433,0.00003593749,0.00006859493,0.0003378561,0.0008866209],"genre_scores_gemma":[0.3033502,0.0008751864,0.68973,0.0001080348,0.00007610349,0.0002562997,0.0005188342,0.0001657431,0.00491962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004621288,"threshold_uncertainty_score":0.009188771,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2127390035","doi":"10.1109/89.902283","title":"Synthetic stereo acoustic echo cancellation structure for multiple participant VoIP conferences","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Monaural; Spatialization; Echo (communications protocol); Computer science; Stereophonic sound; Teleconference; Loudspeaker; Voice over IP; Reverberation; Speech recognition; Channel (broadcasting); Artificial intelligence; Acoustics; Telecommunications; The Internet","authors":[{"name":"Trevor Yensen","is_ca":true},{"name":"Rafik Goubran","is_ca":true},{"name":"Ioannis Lambadaris","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0403782802899136,"gpt":0.2751404288449312,"spread":0.2347621485550176,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003382467,0.0002574263,0.0002043003,0.0002578699,0.0002702461,0.0003020465,0.0006517615,0.0004633529,0.002104058],"category_scores_gemma":[0.0007470513,0.0001285197,0.0002584698,0.0001988476,0.0002489815,0.0004716597,0.0003822289,0.0003564405,0.0008215659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002386925,"about_ca_system_score_gemma":0.0004252635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000402491,"about_ca_topic_score_gemma":0.0007650946,"domain_scores_codex":[0.9996222,0.00008636533,0.00001280231,0.00003510911,0.0002140765,0.0000295031],"domain_scores_gemma":[0.9995733,0.00008828886,0.00003789995,0.00006188003,0.00018941,0.00004913377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"design_other","study_design_scores_codex":[0.0007187905,0.0001802876,0.0007267119,0.0001531023,0.00005011259,0.0002776316,0.0001753835,0.05866694,0.6360421,0.01596703,0.002337066,0.2847047],"study_design_scores_gemma":[0.0001587732,0.001299353,0.001796214,0.00002204078,0.00005926634,0.001142216,0.0001190852,0.7142822,0.2516087,0.005228895,0.0241977,0.00008568262],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08687777,0.0002846921,0.9052621,0.0001601398,0.0001044501,0.00006127563,0.00007407504,0.0007188077,0.006456763],"genre_scores_gemma":[0.6702645,0.0001895027,0.3229832,0.0001750263,0.0001195461,0.00008909962,0.0002620115,0.00007317722,0.005843987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002104058,"threshold_uncertainty_score":0.007038713,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2146814186","doi":"10.1109/tsa.2005.851945","title":"A variable step-size pre-filter-bank adaptive algorithm","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Algorithm; Adaptive filter; Mathematics; Filter (signal processing); Computational complexity theory; Convergence (economics); Autocorrelation; Filter bank; Noise (video); Adaptive algorithm; A priori and a posteriori; Computer science; Statistics; Artificial intelligence","authors":[{"name":"Ting Liu","is_ca":true},{"name":"Saeed Gazor","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01163436432685181,"gpt":0.2348833479658294,"spread":0.2232489836389775,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004731367,0.0005192061,0.0006356748,0.0005034402,0.0003503442,0.0006794701,0.001272017,0.001006197,0.003979674],"category_scores_gemma":[0.001364371,0.0003557894,0.0004718376,0.0005646106,0.0004262904,0.0007216192,0.000618038,0.001080521,0.001317902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000415393,"about_ca_system_score_gemma":0.001299839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002422122,"about_ca_topic_score_gemma":0.002382464,"domain_scores_codex":[0.9995012,0.00007819359,0.00003246743,0.0001311854,0.0002172699,0.00003960006],"domain_scores_gemma":[0.9995723,0.0001527161,0.00003214678,0.00005308774,0.0001710392,0.00001866482],"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":[0.0002188503,0.00009364938,0.0006434541,0.0001464943,0.00007168709,0.0001044632,0.00008745205,0.165751,0.04287871,0.01535188,0.004327428,0.770325],"study_design_scores_gemma":[0.00004923964,0.0001016127,0.0003494232,0.00001332267,0.00001747422,0.0001474436,0.000009504373,0.9815325,0.01015624,0.002062116,0.005541936,0.00001914918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002171602,0.00008205829,0.9965389,0.00004147237,0.00003912574,0.0000352061,0.00001256321,0.0003055816,0.0007734385],"genre_scores_gemma":[0.1216515,0.0002702609,0.8706009,0.0001760155,0.00006882186,0.0002911522,0.0001626296,0.00008849265,0.006690197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003979674,"threshold_uncertainty_score":0.01331335,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2132572274","doi":"10.1109/tsa.2005.851917","title":"LSP quantization by a union of locally trained codebooks","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Speech and Audio Processing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Codebook; Vector quantization; Algorithm; Mathematics; Encoder; Speech coding; Computational complexity theory; Code-excited linear prediction; Speech recognition; Pattern recognition (psychology); Linear predictive coding; Computer science; Artificial intelligence; Statistics","authors":[{"name":"Turaj Zakizadeh Shabestary","is_ca":true},{"name":"P. Hedelin","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01246671299897269,"gpt":0.2608341671625037,"spread":0.248367454163531,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00118314,0.0005381792,0.0007995916,0.00041181,0.0003084451,0.0007502843,0.001513185,0.0006883538,0.003387571],"category_scores_gemma":[0.004050131,0.0003895617,0.000425965,0.0007312929,0.0006800791,0.001912384,0.001267092,0.001186881,0.001857515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005679436,"about_ca_system_score_gemma":0.000660971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002167047,"about_ca_topic_score_gemma":0.002500724,"domain_scores_codex":[0.998968,0.0002591466,0.00006488949,0.0001986764,0.0004543312,0.00005495809],"domain_scores_gemma":[0.9987894,0.0003750206,0.00008447191,0.0003864729,0.0003199825,0.0000447737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003730201,0.00009371246,0.0005050057,0.0001312464,0.00005364954,0.00007352538,0.0001486506,0.4807514,0.03602455,0.04298704,0.005475794,0.4333824],"study_design_scores_gemma":[0.00001625748,0.00003714247,0.00007531137,0.000007655439,0.000004555343,0.00004047985,0.000006625558,0.9885344,0.005921134,0.004146924,0.001200124,0.000009423015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003853699,0.00009479481,0.9949573,0.00003997937,0.00001608596,0.00001872136,0.00003309602,0.0004018549,0.0005845184],"genre_scores_gemma":[0.2187703,0.0002073241,0.775421,0.0001473629,0.00006056332,0.000200785,0.0003493326,0.0001914195,0.004651871],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003387571,"threshold_uncertainty_score":0.01133257,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}