{"id":"W4387759193","doi":"10.1016/j.apacoust.2023.109690","title":"A tensor decomposition based multichannel linear prediction approach to speech dereverberation","year":2023,"lang":"en","type":"article","venue":"Applied Acoustics","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; National Key Research and Development Program of China; Sichuan Province Science and Technology Support Program; National Natural Science Foundation of China","keywords":"Reverberation; Computer science; Intelligibility (philosophy); Algorithm; Linear prediction; Speech recognition; Matrix decomposition; Tensor decomposition; Reduction (mathematics); Kronecker delta; Speech enhancement; Filter (signal processing); Tensor (intrinsic definition); 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":[0.0002545164,0.0001573944,0.0001394094,0.0002119427,0.0002493777,0.0001687663,0.0003401393,0.00009919775,0.000002342731],"category_scores_gemma":[0.00005108851,0.0001577816,0.0000352075,0.001001323,0.00001687352,0.0001737575,0.0001047751,0.0001321633,0.0002857531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006132504,"about_ca_system_score_gemma":0.00006882059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002544021,"about_ca_topic_score_gemma":5.22136e-7,"domain_scores_codex":[0.9986321,0.00001708368,0.0002278446,0.0004403519,0.0003466281,0.0003359654],"domain_scores_gemma":[0.9992833,0.00006958515,0.0000659051,0.0003311205,0.0001168804,0.0001331871],"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":[0.00003113745,0.0002373318,0.00008194222,0.0001179409,0.00001995177,0.00001357407,0.0006543064,0.3870628,0.5443451,0.0008386759,0.007394073,0.05920315],"study_design_scores_gemma":[0.0003702669,0.00003699558,0.0008894624,0.00001712361,0.00001435448,0.00000665398,0.00005576918,0.8777086,0.1198967,0.0006195435,0.0002094037,0.0001751014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03137625,0.000004306635,0.9654462,0.0002858168,0.0002328403,0.0003728701,0.00001094246,0.0008095371,0.001461194],"genre_scores_gemma":[0.5040094,0.000001921243,0.494898,0.0006879758,0.0002014017,0.0000559427,0.00006380639,0.00001429999,0.00006735523],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4906458,"threshold_uncertainty_score":0.6434149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02038834813085267,"score_gpt":0.2614898107352502,"score_spread":0.2411014626043975,"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."}}