{"id":"W3155588681","doi":"10.1109/mim.2021.9400952","title":"Biomedical Diagnosis of Infant Cry Signal Based on Analysis of Cepstrum by Deep Feedforward Artificial Neural Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Instrumentation & Measurement Magazine","topic":"Infant Health and Development","field":"Health Professions","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cepstrum; Computer science; Artificial neural network; Speech recognition; Artificial intelligence; Mel-frequency cepstrum; SIGNAL (programming language); Field (mathematics); Feedforward neural network; Clinical diagnosis; Signal processing; Pattern recognition (psychology); Machine learning; Feature extraction; Digital signal processing; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0002892783,0.0004938695,0.0002943268,0.0006565949,0.0001283095,0.0002885459,0.0002575417,0.0004197413,0.001063442],"category_scores_gemma":[0.0009276024,0.000142699,0.0002568102,0.0002784472,0.000107476,0.0003625213,0.000274729,0.0004736082,0.0004436185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001885159,"about_ca_system_score_gemma":0.000294205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00265666,"about_ca_topic_score_gemma":0.003570587,"domain_scores_codex":[0.9998748,0.00001985997,0.00001007924,0.00002564782,0.00005048095,0.00001895337],"domain_scores_gemma":[0.9998353,0.00005057798,0.00001924286,0.000008822096,0.00007652999,0.00000961008],"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":[0.0006175683,0.0002045427,0.007897805,0.000277433,0.00007655924,0.0007573961,0.0001379364,0.0620387,0.1712796,0.001669035,0.006236522,0.7488069],"study_design_scores_gemma":[0.00001465648,0.0001055254,0.007076632,0.00003166295,0.00003191865,0.0002717614,0.00003634304,0.9686175,0.02193368,0.0007076928,0.001156282,0.00001632214],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2019108,0.003553878,0.7869795,0.0006231869,0.0003207513,0.00009227468,0.0006331948,0.001892418,0.003994028],"genre_scores_gemma":[0.8801496,0.002143877,0.1127654,0.0001811723,0.0001089634,0.00005435076,0.0006561736,0.00006008581,0.003880379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00265666,"threshold_uncertainty_score":0.005282342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05377505315013978,"score_gpt":0.3471300740282298,"score_spread":0.2933550208780901,"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."}}