{"id":"W4381137386","doi":"10.3390/diagnostics13122107","title":"Infant Cry Signal Diagnostic System Using Deep Learning and Fused Features","year":2023,"lang":"en","type":"article","venue":"Diagnostics","topic":"Infant Health and Development","field":"Health Professions","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Spectrogram; Artificial intelligence; Computer science; Cepstrum; Confusion matrix; Crying; Support vector machine; Feature (linguistics); Mel-frequency cepstrum; Deep learning; Convolutional neural network; Artificial neural network; Pattern recognition (psychology); Machine learning; Receiver operating characteristic; Speech recognition; Random forest; Frequency domain; Feature extraction; Medicine","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.000923217,0.0002367653,0.000372951,0.0002507987,0.001626571,0.00002800819,0.0001144972,0.000294487,0.00008981908],"category_scores_gemma":[0.005009095,0.0002159897,0.00004001099,0.0004766827,0.00005831659,0.0000769512,0.0002292485,0.0008909208,0.0005759888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002069036,"about_ca_system_score_gemma":0.000402708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002836886,"about_ca_topic_score_gemma":0.00008972113,"domain_scores_codex":[0.997263,0.0004384172,0.0006160708,0.0003439506,0.000352006,0.0009865151],"domain_scores_gemma":[0.9873872,0.01166833,0.0002269998,0.0001730875,0.0001811234,0.0003632518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000592758,0.00003185983,0.9618888,0.002349385,0.00004739806,0.0004794735,0.009252097,0.0006764689,0.00009233633,0.003758499,0.01564691,0.00571746],"study_design_scores_gemma":[0.002098424,0.0001668792,0.897032,0.002891693,0.0001175583,0.00004049862,0.01679456,0.01405756,0.00007540002,0.0002619095,0.06568838,0.000775174],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874334,0.002569058,0.001188896,0.0005451458,0.002244601,0.001545232,0.00004625278,0.0009148296,0.003512639],"genre_scores_gemma":[0.9935222,0.002462641,0.001514328,0.001058501,0.0006567321,0.000205153,0.0001124728,0.00006489285,0.0004030499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06485687,"threshold_uncertainty_score":0.9996732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03627262373139447,"score_gpt":0.3799165034017074,"score_spread":0.343643879670313,"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."}}