{"id":"W2134423147","doi":"10.1109/icassp.1990.115616","title":"Nonlinear multiplicative cepstral analysis for pitch extraction in speech","year":2002,"lang":"en","type":"article","venue":"International Conference on Acoustics, Speech, and Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Nonlinear system; Speech recognition; Cepstrum; Computer science; Frequency domain; Multiplicative function; Harmonics; Waveform; Nonlinear distortion; Artificial intelligence; Mathematics; Engineering; Physics; Telecommunications; Mathematical analysis; Computer vision","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":[0.0003950612,0.0006570632,0.0003666373,0.0005835757,0.0003204785,0.000750956,0.0004554618,0.0004605458,0.002870888],"category_scores_gemma":[0.001467788,0.0002233633,0.0004156138,0.0006283422,0.0001872612,0.0006405767,0.0004229173,0.0006244495,0.001937817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003423395,"about_ca_system_score_gemma":0.0003645532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002556612,"about_ca_topic_score_gemma":0.003722032,"domain_scores_codex":[0.999774,0.00005970047,0.000014667,0.00004177188,0.00009311366,0.00001670329],"domain_scores_gemma":[0.9997585,0.0001049046,0.00001574247,0.00003865661,0.00007347737,0.000008659744],"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":[0.0002846851,0.00008387875,0.0006014968,0.0003667982,0.0000839792,0.000283221,0.0001361944,0.1358089,0.2093613,0.0253548,0.005743556,0.6218912],"study_design_scores_gemma":[0.000008677303,0.00004816421,0.0005616232,0.00001540838,0.00002798198,0.0001143341,0.00001780476,0.971054,0.01951835,0.002507431,0.006108561,0.00001764135],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01201121,0.0006505121,0.9848006,0.0001190489,0.00007247004,0.00004694692,0.0002124876,0.0006646734,0.001422095],"genre_scores_gemma":[0.2641361,0.001847993,0.7243375,0.0001018839,0.0001733642,0.0001824593,0.001050073,0.0002924639,0.007878092],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002870888,"threshold_uncertainty_score":0.009604096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0569273630214847,"score_gpt":0.3191739438690052,"score_spread":0.2622465808475205,"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."}}