Cited by 58Infant Health and DevelopmentArtificial Intelligence in HealthcareMusic and Audio Processing

Cry-based infant pathology classification using GMMs

Open access
Farsaie Alaie, Hesam, Abou-Abbas, Lina, Tadj, Chakib·2016·Speech Communication

Abstract

Traditional studies of infant cry signals focus more on non-pathology-based classification of infants. In this paper, we introduce a noninvasive health care system that performs acoustic analysis of unclean noisy infant cry signals to extract and measure certain cry characteristics quantitatively and classify healthy and sick newborn infants according to only their cries. In the conduct of this newborn cry-based diagnostic system, the dynamic MFCC features along with static Mel-Frequency Cepstral Coefficients (MFCCs) are selected and extracted for both expiratory and inspiratory cry vocalizations to produce a discriminative and informative feature vector. Next, we create a unique cry pattern for each cry vocalization type and pathological condition by introducing a novel idea using the Boosting Mixture Learning (BML) method to derive either healthy or pathology subclass models separately from the Gaussian Mixture Model-Universal Background Model (GMM-UBM). Our newborn cry-based diagnostic system (NCDS) has a hierarchical scheme that is a treelike combination of individual classifiers. Moreover, a score-level fusion of the proposed expiratory and inspiratory cry-based subsystems is performed to make a more reliable decision. The experimental results indicate that the adapted BML method has lower error rates than the Bayesian approach or the maximum a posteriori probability (MAP) adaptation approach when considered as a reference method.

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Crossref · 10.1016/j.specom.2015.12.001

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  • SCG measurements are reported on adult populations, but their use in routine clinical practice is rare and absent in neonatal and/or pediatric contexts due at least partly to high chest curvature and fragile skin surface.

    “Although SCG measurements are reported on adult populations50, their use in routine clinical practice is rare and is absent in neonatal and/or pediatric contexts due, at least partly, to the high degrees of curvature of the chest51 and the fragility of the skin surface52,53.”

    From Skin-interfaced biosensors for advanced wireless physiological monitoring in neonatal and pediatric intensive-care units (2020) · cites this paper as [53]

    The Introduction (page unavailable) discusses infant cry signals as a way to identify pathology, and the Recording procedure section (page unavailable) describes recordings of newborn cries. Searches for SCG, chest curvature, skin fragility, and routine clinical use found no passage addressing the attributed explanation for the absence of neonatal or pediatric SCG measurements. (LLM, conf=0.75)

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Research works printed but not linked: 4

  1. Kolcz A. Chowdhury A. Alspector J. Data duplication: an imbalance problem ? presented at the Twentieth International Conference on Machine Learning (ICML-2003) Workshop on Learning from Imbalanced Data Sets II 2003
  2. Lederman D., “Automatic Classification of Infants' Cry” Master of Science Master of Science Thesis, Department of Electrical and Computer Engineering BEN-GURION University of the NEGEV 2002.
  3. Verlinde P. Cholet G. Comparing decision fusion paradigms using k-NN based classifiers, decision trees and logistic regression in a multi-modal identity verification application Proceedings of the AVBPA 1999 188 193
  4. Young S.J. Evermann G. Gales M.J.F. Hain T. Kershaw D. Moore G. The HTK Book (for HTK version 3.4) 2006 Cambridge University Engineering Department

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Authors
Farsaie Alaie, H., Abou-Abbas, L., & Tadj, C.
Affiliations
Université du Québec (CA)
Venue
Speech Communication
Publisher
Elsevier BV
Published
2016-03
Volume
77
Pages
28-52
ISSN
0167-6393 (ISSN-L)
OpenAlex
W2259135914
Cited by
58
References
71
Citations by year
2026: 1 · 2025: 4 · 2024: 3 · 2023: 12 · 2022: 15 · 2021: 7 · 2020: 4 · 2019: 3 · 2018: 5 · 2017: 2 · 2016: 2
FWCI
7.57
Type
journal article
Language
en
Open access
Locations
  • Speech Communication (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Bill and Melinda Gates Foundation
Topics
  • Health Sciences › Health Professions › Pharmacy › Infant Health and Development
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
Keywords
infant crying, Gaussian mixture model, GMM-UBM, Mel-frequency cepstral coefficients, score-level fusion, hierarchical classifier, noisy speech

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