{"id":"W2374371280","doi":"","title":"Wavelet Package Speech Enhancement Based on Mask Property Shrink","year":2010,"lang":"en","type":"article","venue":"Communications technology","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Computer science; Wavelet; PESQ; Masking (illustration); Wavelet packet decomposition; Speech recognition; Second-generation wavelet transform; Speech enhancement; Property (philosophy); Noise (video); Wavelet transform; Discrete wavelet transform; Artificial intelligence; Stationary wavelet transform; Pattern recognition (psychology); Noise reduction","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":[0.0002638129,0.0003897031,0.0004312104,0.0003060519,0.0001446424,0.0003152625,0.0002939613,0.0003104758,0.00191104],"category_scores_gemma":[0.0006129781,0.0001568354,0.0003947807,0.0002631861,0.0002627143,0.0006494518,0.0004538071,0.0003778833,0.000698048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001011126,"about_ca_system_score_gemma":0.0001184725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001843652,"about_ca_topic_score_gemma":0.0002568506,"domain_scores_codex":[0.999844,0.00002421415,0.000008392076,0.00002965474,0.0000827803,0.00001091394],"domain_scores_gemma":[0.9997861,0.00006991809,0.00002322745,0.0000453871,0.0000632088,0.00001221374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003464118,0.00004516466,0.0003373548,0.0001406846,0.00003030471,0.0001893566,0.0001195394,0.01377858,0.7480888,0.01004336,0.0009179595,0.2259625],"study_design_scores_gemma":[0.0000523242,0.0004168736,0.001941958,0.00001950919,0.00007437655,0.001072164,0.00004776077,0.4022374,0.5732934,0.003599039,0.01720287,0.0000422319],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03561977,0.0002687938,0.96148,0.00005574811,0.00005492272,0.00002585972,0.00002900502,0.0004919624,0.001973994],"genre_scores_gemma":[0.3798562,0.0009176103,0.6086555,0.00009570791,0.0001138246,0.00008002486,0.0001666473,0.0002076941,0.009906691],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00191104,"threshold_uncertainty_score":0.006393075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0184827388576698,"score_gpt":0.2712487085612684,"score_spread":0.2527659697035986,"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."}}