{"id":"W2023604838","doi":"10.1109/icabme.2015.7323302","title":"On the use of EMD for automatic newborn cry segmentation","year":2015,"lang":"en","type":"article","venue":"","topic":"Infant Health and Development","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; École de Technologie Supérieure","funders":"Bill and Melinda Gates Foundation","keywords":"Hilbert–Huang transform; Hidden Markov model; Speech recognition; Segmentation; Computer science; Preprocessor; Artificial intelligence; Pattern recognition (psychology); Mel-frequency cepstrum; Crying; Noise (video); Cepstrum; SIGNAL (programming language); Feature extraction; Computer vision; Psychology","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":[0.0007080358,0.00005931202,0.000105678,0.00003999584,0.0002328253,0.00000355523,0.00005707342,0.00006052567,0.0003654477],"category_scores_gemma":[0.0005789408,0.00003303824,0.00002044956,0.00007334039,0.00001286458,0.00006381056,0.00002107925,0.0000995728,0.0002174036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000952034,"about_ca_system_score_gemma":0.0006072475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001566793,"about_ca_topic_score_gemma":0.00006372113,"domain_scores_codex":[0.9989948,0.000160835,0.0003736862,0.00007970597,0.0001647529,0.0002261893],"domain_scores_gemma":[0.9979967,0.001424273,0.0001458573,0.0001603661,0.0001748574,0.00009797452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001491032,0.00005210039,0.004063851,0.0003283379,0.00001444407,2.589892e-7,0.00933835,0.00003333735,0.00005018339,0.0462628,0.9220884,0.01761883],"study_design_scores_gemma":[0.006944835,0.001008546,0.05334498,0.0007576332,0.00004872721,0.00000124575,0.02320554,0.03636246,0.001274595,0.0280518,0.8485345,0.0004651838],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.891333,0.00002190165,0.0491389,0.01784929,0.001847866,0.006377313,0.00004367882,0.0002004288,0.03318756],"genre_scores_gemma":[0.4550931,0.00002542536,0.3900771,0.1138939,0.0004411206,0.002480098,0.0001543586,0.00007174218,0.03776319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.43624,"threshold_uncertainty_score":0.4001396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3419600378155519,"score_gpt":0.4706113318447669,"score_spread":0.128651294029215,"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."}}