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Related Experiment Video

Updated: Mar 15, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Expiratory and Inspiratory Cries Detection Using Different Signals' Decomposition Techniques.

Lina Abou-Abbas1, Chakib Tadj1, Christian Gargour1

  • 1Electrical Engineering Department, École de Technologie Supérieure, Montreal, Canada.

Journal of Voice : Official Journal of the Voice Foundation
|August 28, 2016
PubMed
Summary

This study introduces automatic infant cry analysis by segmenting cry signals to detect breathing phases. Empirical Mode Decomposition (EMD) with specific intrinsic mode functions (IMFs) achieved the lowest classification error rates.

Keywords:
Gaussian mixture modelsautomatic segmentationempirical mode decompositionhidden Markov modelswavelet packet transform

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Infant Health Monitoring

Background:

  • Accurate infant cry analysis is crucial for understanding infant health and development.
  • Automating the segmentation of cry signals aids in objective and efficient analysis.

Purpose of the Study:

  • To develop and evaluate an automatic system for segmenting infant cry signals.
  • To detect expiratory and inspiratory phases within cry signals.
  • To compare the performance of Empirical Mode Decomposition (EMD) against other signal decomposition techniques.

Main Methods:

  • The study employed a three-stage approach: signal decomposition, feature extraction, and classification.
  • Signal decomposition techniques considered included short-time Fourier transform, EMD, and wavelet packet transform.
  • Supervised learning classifiers, Gaussian mixture models (GMM) and hidden Markov models (HMM), were utilized for classification.

Main Results:

  • Empirical Mode Decomposition (EMD) showed competitive performance in cry signal segmentation.
  • A combination of intrinsic mode functions (IMFs) 3, 4, and 5 (IMF3+IMF4+IMF5) from EMD yielded the best results.
  • The lowest global classification error rates achieved were approximately 8.9% with GMM and 11.06% with HMM.

Conclusions:

  • The proposed system effectively segments infant cry signals, enabling the detection of breathing phases.
  • EMD, particularly using IMF3+IMF4+IMF5, is a viable and effective method for cry signal decomposition in this context.
  • The study demonstrates the potential of automated cry analysis for objective infant health assessment.