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.

References

M. Hariharan, S. Yaacob, and S. A. Awang, “Pathological infant cry analysis using wavelet packet transform and probabilistic neural network,” Expert Syst. Appl., vol. 38, no. 12, pp. 15377–15382, 2011.

M. Hariharan, L. S. Chee, and S. Yaacob, “Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network,” J. Med. Syst., vol. 36, no. 3, pp. 1309– 1315, 2012.

M. Hariharan, J. Saraswathy, R. Sindhu, W. Khairunizam, and S. Yaacob, “Infant cry classification to identify asphyxia using timefrequency analysis and radial basis neural networks,” Expert Syst. Appl., vol. 39, no. 10, pp. 9515–9523, 2012.

Y. Abdulaziz and S. M. S. Ahmad, “Infant cry recognition system: A comparison of system performance based on mel frequency and linear prediction cepstral coefficients,” Information Retrieval & Knowledge Management, (CAMP), 2010 International Conference on. pp. 260– 263, 2010.

R. Sahak, W. Mansor, Y. K. Lee, A. I. M. Yassin, and A. Zabidi, “Performance of combined support vector machine and principal component analysis in recognizing infant cry with asphyxia,” in Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, 2010, pp. 6292–6295.

S. Orlandi, C. A. Reyes Garcia, A. Bandini, G. Donzelli, and C. Manfredi, “Application of Pattern Recognition Techniques to the Classification of Full-Term and Preterm Infant Cry,” Journal of Voice, 2015.

A. Zabidi, W. Mansor, Y. K. Lee, I. M. Yassin, and R. Sahak, “Binary Particle Swarm Optimization for selection of features in the recognition of infants cries with asphyxia,” Signal Processing and its Applications (CSPA), 2011 IEEE 7th International Colloquium on. pp. 272–276, 2011.

C.-Y. Chang, C.-W. Chang, S. Kathiravan, C. Lin, and S.-T. Chen, “DAG-SVM based infant cry classification system using sequential forward floating feature selection,” Multidimens. Syst. Signal Process., pp. 1–16, 2016.

O. F. R. Galaviz and C. A. R. García, “Infant Cry Classification to Identify Hypo Acoustics and Asphyxia Comparing an EvolutionaryNeural System with a Neural Network System,” in MICAI 2005: Advances in Artificial Intelligence: 4th Mexican International Conference on Artificial Intelligence, Monterrey, Mexico, November 14-18, 2005. Proceedings, A. Gelbukh, Á. de Albornoz, and H. Terashima-Marín, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, pp. 949–958.

S. E. Barajas and C. A. Reyes, “Your Fuzzy Relational Neural Network Parameters Optimization with a Genetic Algorithm,” Fuzzy Systems, 2005. FUZZ ’05. The 14th IEEE International Conference on. pp. 684– 689, 2005.

A. Rosales-Pérez, C. A. Reyes-García, and P. Gómez-Gil, “Genetic fuzzy relational neural network for infant cry classification,” in Pattern Recognition, Springer, 2011, pp. 288–296.

A. Rosales-Pérez, C. A. Reyes-García, J. A. Gonzalez, O. F. ReyesGalaviz, H. J. Escalante, and S. Orlandi, “Classifying infant cry patterns by the Genetic Selection of a Fuzzy Model,” Biomed. Signal Process. Control, vol. 17, pp. 38–46, 2015.

O. F. Reyes-Galaviz, S. D. Cano-Ortiz, and C. A. Reyes-García, “Evolutionary-Neural System to Classify Infant Cry Units for Pathologies Identification in Recently Born Babies,” 2008 Seventh Mex. Int. Conf. Artif. Intell., pp. 330–335, 2008.

C. F. Z. Boukydis and B. M. Lester, “Infant crying: Theoretical and research perspectives”. Springer Science & Business Media, 2012.

W. Ting, Y. Guo-zheng, Y. Bang-hua, and S. Hong, “EEG feature extraction based on wavelet packet decomposition for brain computer interface,” Measurement, vol. 41, no. 6, pp. 618–625, 2008.

I. Bayram and I. W. Selesnick, “On the Dual-Tree Complex Wavelet Packet and M-Band Transforms,” IEEE Trans. Signal Process., vol. 56, no. 6, pp. 2298–2310, 2008.

Y. Saeys, I. Inza, and P. Larrañaga, “A review of feature selection techniques in bioinformatics,” Bioinformatics, vol. 23, no. 19. pp. 2507–2517, 2007.

M. A. Hall, “Correlation-based feature selection for machine learning.” The University of Waikato, 1999.

M. A. Hall and L. A. Smith, “Feature subset selection: a correlation based filter approach,” 1997.

I. T. Jolliffe, “Principal Component Analysis.”. Springer New York, 1986.

J. Orozco and C. A. R. García, “Detecting pathologies from infant cry applying scaled conjugate gradient neural networks,” in European Symposium on Artificial Neural Networks, Bruges (Belgium), 2003, pp. 349–354.

Y. Yang and J. O. Pedersen, “A Comparative Study on Feature Selection in Text Categorization,” Proc. Fourteenth Int. Conf. Mach. Learn., pp. 412–420, 1997.

N. Spolaôr, E. A. Cherman, M. C. Monard, and H. D. Lee, “Filter Approach Feature Selection Methods to Support Multi-label Learning Based on ReliefF and Information Gain,” in Advances in Artificial Intelligence - SBIA 2012: 21th Brazilian Symposium on Artificial Intelligence, Curitiba, Brazil, October 20-25, 2012. Proceedings, L. N. Barros, M. Finger, A. T. Pozo, G. A. Gimenénez-Lugo, and M. Castilho, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 72–81.

G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme Learning Machine for Regression and Multiclass Classification.,” IEEE Trans. Syst. Man. Cybern. B. Cybern., vol. 42, no. x, pp. 1–17, 2011.

R. Kohavi, “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection,” Int. Jt. Conf. Artif. Intell., vol. 14, no. 12, pp. 1137–1143, 1995.

S. D. C. Ortiz, D. I. E. Beceiro, and T. Ekkel, “A radial basis function network oriented for infant cry classification,” in Progress in Pattern Recognition, Image Analysis and Applications, Springer, 2004, pp. 374–380.

Downloads

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

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.


Most read articles by the same author(s)