{"id":"W3186897582","doi":"10.1109/icjece.2021.3075373","title":"Empirical Mode Decomposition for Analysis and Filtering of Speech Signals","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"King Khalid University","keywords":"Hilbert–Huang transform; Speech recognition; Speech processing; Computer science; Interval (graph theory); Stationary process; Mode (computer interface); SIGNAL (programming language); Context (archaeology); Piecewise; Signal processing; Nonlinear system; Mathematics; Filter (signal processing); Telecommunications; Physics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009826861,0.0009109111,0.0006009635,0.001217493,0.0002980363,0.000823423,0.0005769647,0.0008871835,0.003664405],"category_scores_gemma":[0.002882017,0.0003181818,0.0007250247,0.001767591,0.0004203888,0.0008191597,0.0006676316,0.001391324,0.002187225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002904415,"about_ca_system_score_gemma":0.0004530961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001487369,"about_ca_topic_score_gemma":0.001418495,"domain_scores_codex":[0.9994372,0.00016516,0.0000438773,0.0001132222,0.0002163324,0.00002418536],"domain_scores_gemma":[0.999345,0.0003450745,0.00005900618,0.0001016104,0.0001290128,0.00002030621],"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":[0.0001322391,0.00007978234,0.0007939378,0.0005714959,0.0001122025,0.0003589304,0.000247518,0.05555205,0.05887698,0.04388063,0.008306541,0.8310877],"study_design_scores_gemma":[0.00002544631,0.0001357001,0.003273819,0.000187732,0.00004931834,0.0005810425,0.0001239013,0.8907434,0.01682765,0.04071189,0.04725068,0.00008938816],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001586992,0.001670329,0.9954202,0.00006843934,0.00007527265,0.00003508364,0.000101019,0.0003470817,0.00069557],"genre_scores_gemma":[0.04316667,0.004103585,0.9482519,0.000073256,0.0001836618,0.0002056352,0.0005651828,0.0002131089,0.003236991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003664405,"threshold_uncertainty_score":0.01225865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009942940168084429,"score_gpt":0.2961931763111372,"score_spread":0.2862502361430527,"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."}}