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Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources
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In: https://hal.inria.fr/hal-03550289 ; 2022 (2022)
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Both the validity of the cultural tightness index and the association with creativity and order are spurious -- a comment on Jackson et al ...
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Elastic Full Procrustes Analysis of Plane Curves via Hermitian Covariance Smoothing ...
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Quantitative Evaluation Approach for Translation of Perceptual Soundscape Attributes: Initial Application to the Thai Language ...
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Évaluation de dispositifs numériques innovants pour l’apprentissage de la lecture et de l’anglais : une expérimentation longitudinale en condition écologique
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In: SFERE 2021 - 2ème édition du Colloque de SFERE-Provence ; https://hal.univ-grenoble-alpes.fr/hal-03187570 ; SFERE 2021 - 2ème édition du Colloque de SFERE-Provence, Mar 2021, Marseille, France (2021)
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Vers une modélisation graphique des applications ubiquitaires basée sur un Dsml intelligent : Covid-19 Contact-Tracer
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In: Colloque sur les Objets et systèmes Connectés - COC'2021 ; https://hal.archives-ouvertes.fr/hal-03593726 ; Colloque sur les Objets et systèmes Connectés - COC'2021, IUT d'Aix-Marseille, Mar 2021, MARSEILLE, France (2021)
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Evolution and differentiation of the cybersecurity communities in three social question and answer sites: A mixed-methods analysis.
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In: PloS one, vol 16, iss 12 (2021)
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A DMAIC integrated fuzzy FMEA model: A case study in the automotive industry
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Repeated undersampling in PrInDT (RePrInDT): Variation in undersampling and prediction, and ranking of predictors in ensembles ...
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Combining Prediction and Interpretation in Decision Trees (PrInDT) -- a Linguistic Example ...
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Damping effect in innovation processes: case studies from Twitter ...
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Accent translation : improving online learning experience in Africa
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SautiLearn : improving online learning experience with accent translation
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Bayesian Markov Renewal Mixed Models for Vocalization Syntax ...
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Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?
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Parga, Joanna J; Lewin, Sharon; Lewis, Juanita; Montoya-Williams, Diana; Alwan, Abeer; Shaul, Brianna; Han, Carol; Bookheimer, Susan Y; Eyer, Sherry; Dapretto, Mirella; Zeltzer, Lonnie; Dunlap, Lauren; Nookala, Usha; Sun, Daniel; Dang, Bianca H; Anderson, Ariana E
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In: Pediatric research, vol 87, iss 3 (2020)
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Abstract:
BackgroundTo characterize acoustic features of an infant's cry and use machine learning to provide an objective measurement of behavioral statein a cry-translator. To apply thecry-translation algorithm to colic hypothesizing that these cries sound painful.MethodsAssessment of 1000 cries in a mobile app (ChatterBabyTM). Training a cry-translationalgorithm by evaluating >6000 acoustic features to predict whether infant cry was due to a pain (vaccinations, ear-piercings), fussy, or hunger states. Using the algorithm to predict the behavioral state of infants with reported colic.ResultsThe cry-translationalgorithm was 90.7% accurate for identifying pain cries, and achieved 71.5% accuracy in discriminating cries from fussiness, hunger, or pain. The ChatterBabycry-translationalgorithm overwhelmingly predicted that colic cries were most likely from pain, compared to fussy and hungry states. Colic cries had average pain ratings of 73%, significantly greater than the pain measurements found in fussiness and hunger (p < 0.001, 2-sample t test). Colic cries outranked pain cries by measures of acoustic intensity, including energy, length of voiced periods, and fundamental frequency/pitch, while fussy and hungry cries showed reduced intensity measures compared to pain and colic.ConclusionsAcoustic features of cries are consistent across a diverse infant population and can be utilized as objective markers of pain, hunger, and fussiness. TheChatterBaby algorithm detected significant acoustic similarities between colic and painful cries, suggesting that they may share a neuronal pathway.
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Keyword:
Abdominal Pain; Acoustics; Automated; Colic; Computer-Assisted; Crying; Female; Humans; Infant; Infant Behavior; Machine Learning; Male; Mobile Applications; Newborn; Paediatrics and Reproductive Medicine; Pain Perception; Pattern Recognition; Pediatrics; Public Health and Health Services; Signal Processing; Sound Spectrography
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URL: https://escholarship.org/uc/item/3h36c0hh
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