{"id":"W4200627709","doi":"10.1016/j.bspc.2021.103434","title":"Automated newborn cry diagnostic system using machine learning approach","year":2021,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Infant Health and Development","field":"Health Professions","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Bill and Melinda Gates Foundation","keywords":"Mel-frequency cepstrum; Infant crying; Feature (linguistics); Artificial intelligence; Computer science; Set (abstract data type); Prosody; Feature extraction; Support vector machine; Pattern recognition (psychology); Speech recognition; Feature vector; Machine learning; Crying; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001835161,0.0003481883,0.0005587541,0.0006518594,0.0002744589,0.0003324233,0.0004692464,0.0004918159,0.001630353],"category_scores_gemma":[0.0003565261,0.0001400174,0.0002753537,0.0002564119,0.00006511927,0.0002608118,0.0002489744,0.0003452646,0.0006528551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002661912,"about_ca_system_score_gemma":0.0004417887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00219064,"about_ca_topic_score_gemma":0.002619346,"domain_scores_codex":[0.9998491,0.00001440032,0.00001160731,0.00004401309,0.00005477931,0.0000261448],"domain_scores_gemma":[0.9998216,0.00003981174,0.00001752284,0.00001037025,0.00009876738,0.0000118837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006354786,0.0005744203,0.01451174,0.0001874727,0.0000890985,0.0007063813,0.00006834233,0.01896975,0.1102992,0.0007154092,0.008285267,0.8449575],"study_design_scores_gemma":[0.00008194042,0.0005343197,0.02307017,0.00003814987,0.0001188212,0.001043358,0.00009469425,0.9114983,0.05774241,0.000806779,0.004928196,0.00004289194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3064647,0.001844917,0.6736557,0.0004468525,0.0004679495,0.0002949531,0.001064064,0.007806077,0.007954786],"genre_scores_gemma":[0.8740723,0.0004717867,0.1163209,0.0002232159,0.0001199704,0.0001460025,0.0009225739,0.00004240019,0.007680957],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00219064,"threshold_uncertainty_score":0.005454123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02638727020039237,"score_gpt":0.3346106481958181,"score_spread":0.3082233779954258,"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."}}