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Modelling and Simulation in Engineering
Volume 2012, Article ID 983147, 10 pages
http://dx.doi.org/10.1155/2012/983147
Research Article

Cry-Based Classification of Healthy and Sick Infants Using Adapted Boosting Mixture Learning Method for Gaussian Mixture Models

École de Technologie Supérieure, Université du Québec, 1100 rue Notre-Dame Ouest, Montréal, QC, Canada H3C 1K3

Received 28 August 2012; Revised 21 November 2012; Accepted 30 November 2012

Academic Editor: Shinsuke Hara

Copyright © 2012 Hesam Farsaie Alaie and Chakib Tadj. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [6 citations]

The following is the list of published articles that have cited the current article.

  • Lina Abou-Abbas, Hesam Fersai Alaei, and Chakib Tadj, “Segmentation of voiced newborns' cry sounds using wavelet packet based features,” 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 796–800, . View at Publisher · View at Google Scholar
  • Stavros Ntalampiras, “Audio pattern recognition of baby crying sound events,” AES: Journal of the Audio Engineering Society, vol. 63, no. 5, pp. 358–369, 2015. View at Publisher · View at Google Scholar
  • Ştefan Stelian Diaconescu, Gheorghiţa Sardescu, Elvira Brətilə, and Mircea Sorin Rusu, “Database and system design for data collection of crying related to infant's needs and diseases,” 2015 International Conference on Speech Technology and Human-Computer Dialogue, SpeD 2015, 2015. View at Publisher · View at Google Scholar
  • Hesam Farsaie Alaie, Lina Abou-Abbas, and Chakib Tadj, “Cry-based infant pathology classification using GMMs,” Speech Communication, vol. 77, pp. 28–52, 2016. View at Publisher · View at Google Scholar
  • Silvia Orlandi, Claudia Manfredi, Carlos Alberto Reyes Garcia, Andrea Bandini, and Gianpaolo Donzelli, “Application of Pattern Recognition Techniques to the Classification of Full-Term and Preterm Infant Cry,” Journal of Voice, vol. 30, no. 6, pp. 656–663, 2016. View at Publisher · View at Google Scholar
  • Sindhu, Vikneswaran Vijean, Thiyagar Nadarajaw, Kemal Polat, Hariharan, Haniza Yazid, and Sazali Yaacob, “Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification,” Computer Methods and Programs in Biomedicine, vol. 155, pp. 39–51, 2018. View at Publisher · View at Google Scholar