Dual-Tree Complex Wavelet Packet Transform and Feature Selection Techniques for Infant Cry Classification

Authors

  • Wei Jer Lim School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 02600, Perlis, Malaysia.
  • Hariharan Muthusamy Department of Biomedical Engineering SRM University, Kattan kulathur – 603 203, Tamil Nadu, India.
  • Vikneswaran Vijean School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 02600, Perlis, Malaysia.
  • Haniza Yazid School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), 02600, Perlis, Malaysia.
  • Thiyagar Nadarajaw Head of Department, Consultant Pediatrician & Adolescent, Medicine Specialist, Department of Pediatrics, Hospital Sultanah Bahiyah, Alor Setar, Kedah.
  • Sazali Yaacob Universiti Kuala Lumpur Malaysian Spanish Institute, Kulim Hi-Tech Park, 09000 Kulim, Kedah, Malaysia.

Keywords:

Infant Cry Classification, Feature Selection, Dual-Tree Complex Wavelet Packet Transform,

Abstract

A Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) feature extraction has been used in infant cry signal classification to extract the feature. Total of 124 energy features and 124 Shannon entropy features were extracted from each sub-band after five level decomposition by DT-CWPT. Feature selection techniques used to deal with massive information obtained from DT-CWPT extraction. The feature selection techniques reduced the number of features by select and form feature subset for classification phase. ELM classifier with 10-fold cross-validation scheme was used to classify the infant cry signal. Three experiments were conducted with different feature sets for three binary classification problems (Asphyxia versus Normal, Deaf versus Normal, and Hunger versus Pain). The results reported that features selection techniques reduced the number of features and achieved high accuracy.

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Published

2018-05-30

How to Cite

Lim, W. J., Muthusamy, H., Vijean, V., Yazid, H., Nadarajaw, T., & Yaacob, S. (2018). Dual-Tree Complex Wavelet Packet Transform and Feature Selection Techniques for Infant Cry Classification. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-16), 75–79. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4098

Issue

Vol. 10 No. 1-16: Recent Trends in Biomedical Engineering Systems and Technologies I

Section

Articles

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