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Expiratory and inspiratory cries detection using different signals' decomposition techniques

Lina Abou-Abbas, Chakib Tadj, Christian Gargour and Leila Montazeri

Article (2017)

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Cite this document: Abou-Abbas, L., Tadj, C., Gargour, C. & Montazeri, L. (2017). Expiratory and inspiratory cries detection using different signals' decomposition techniques. Journal of Voice, 31(2), 259.E13-259.E28. doi:10.1016/j.jvoice.2016.05.015
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Abstract

This paper addresses the problem of automatic cry signal segmentation for the purposes of infant cry analysis. The main goal is to automatically detect expiratory and inspiratory phases from recorded cry signals. The approach used in this paper is made up of three stages: signal decomposition, features extraction, and classification. In the first stage, short-time Fourier transform, empirical mode decomposition (EMD), and wavelet packet transform have been considered. In the second stage, various set of features have been extracted, and in the third stage, two supervised learning methods, Gaussian mixture models and hidden Markov models, with four and five states, have been discussed as well. The main goal of this work is to investigate the EMD performance and to compare it with the other standard decomposition techniques. A combination of two and three intrinsic mode functions (IMFs) that resulted from EMD has been used to represent cry signal. The performance of nine different segmentation systems has been evaluated. The experiments for each system have been repeated several times with different training and testing datasets, randomly chosen using a 10-fold cross-validation procedure. The lowest global classification error rates of around 8.9% and 11.06% have been achieved using a Gaussian mixture models classifier and a hidden Markov models classifier, respectively. Among all IMF combinations, the winner combination is IMF3+IMF4+IMF5.

Uncontrolled Keywords

Gaussian mixture models; automatic segmentation; empirical mode decomposition; hidden Markov models; wavelet packet transform; Acoustics; Crying; Databases, Factual; Exhalation; Female; Humans; Infant; Infant Behavior; Infant, Newborn; Inhalation; Male; Markov Chains; Pattern Recognition, Automated; Signal Processing, Computer-Assisted; Sound Spectrography; Voice Quality; Wavelet Analysis

Open Access document in PolyPublie
Subjects: 2500 Génie électrique et électronique > 2500 Génie électrique et électronique
2500 Génie électrique et électronique > 2514 Traitement des signaux numériques
Department: Département de génie électrique
Research Center: Non applicable
Funders: Bill and Melinda Gates Foundation
Grant number: OPP1067980
Date Deposited: 07 Dec 2018 13:41
Last Modified: 08 Dec 2018 01:20
PolyPublie URL: https://publications.polymtl.ca/3530/
Document issued by the official publisher
Journal Title: Journal of Voice (vol. 31, no. 2)
Publisher: Elsevier
Official URL: https://doi.org/10.1016/j.jvoice.2016.05.015

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