Cry-based infant pathology classification using GMMs
Open accessAbstract
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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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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Classification of Infant Behavioural Traits using Acoustic Cry: An Empirical Study
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Infant Cry Classification Using Compression-Based Similarity Metric
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Robustness of Whisper Features for Infant Cry Classification
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Self-citationA Method for Modelling and Simulation the Changes Trend of Emotions in Human Speech
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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)
- PMC
- PMC4971135
- 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
- hybrid
- doi.org
- Licence
- 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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