{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003558273,0.0006815991,0.0006740909,0.0007259481,0.0001890787,0.0004079023,0.0006091656,0.0006360518,0.001388666],"category_scores_gemma":[0.0008431918,0.0001947968,0.0004224424,0.0002746845,0.00009785574,0.0005199064,0.0006792432,0.0006089258,0.0005660637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004343534,"about_ca_system_score_gemma":0.0004384644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003072737,"about_ca_topic_score_gemma":0.003257046,"domain_scores_codex":[0.9997682,0.00002303874,0.00002342256,0.00006842304,0.00007494517,0.00004186479],"domain_scores_gemma":[0.9997573,0.00005201582,0.00003050215,0.00001815716,0.0001176761,0.00002427886],"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":[0.001277536,0.0006054426,0.01458117,0.000334354,0.0002089231,0.001205,0.0001256426,0.02959255,0.1302501,0.0008263874,0.009412329,0.8115805],"study_design_scores_gemma":[0.00008532403,0.0005923277,0.01086683,0.00004646159,0.0001343754,0.000771993,0.00007560026,0.9177342,0.06541679,0.00106235,0.003159361,0.0000543729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.372928,0.003090742,0.5984947,0.0008228882,0.000444689,0.0003552982,0.001997351,0.01694086,0.00492548],"genre_scores_gemma":[0.8678115,0.0006228748,0.1239163,0.0005531313,0.00009476924,0.0001738808,0.001951489,0.00007543134,0.004800595],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003072737,"threshold_uncertainty_score":0.006109715,"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."}}