Rohilah Sahak, Wahidah Mansor, Khuan Y. Lee, Azlee Zabidi, and Ahmad I.M. Yassin


  1. [1] G. Varallyay Jr., Future prospects of the application of theinfant cry in the medicine, Periodica Polytechnica Ser. El.Eng., 50(1–2), 2006, 47–62.
  2. [2] M. Petroni, A.S. Malowany, C.C. Johnston, and B.J. Stevens,Classification of infant cry vocalizations using artificial neuralnetworks, International Conference on Acoustics, Speech andSignal Processings, 5, 1995, 3475–3478.
  3. [3] K. Manicknam and H. Li, Complexity analysis of normaland deaf infant cry acoustic waves, Proc. 4th Int. Workshopon Model and Analysis of Vocal Emission for BiomedicalApplications (MAVEBA), Florence, 2005, 102–108.
  4. [4] A. Ismaelli, G. Rapisardi, G.P. Donzelli, M. Moroni, andP. Bruscaglioni, A new device for computerized infant cryanalysis in the NICU, Proc. 16th Annual Int. Conf. of theIEEE, 1994, 854–855.
  5. [5] M. Laberge, Infancy through adolescence gale (FarmingtonHills, MI: Gale Group, Thomson Gale, 2006).
  6. [6] D. Lederman, Automatic classification of infants’ cry, M.Sc.degree, Ben-Gurion University of the Negev, October 2002.
  7. [7] O. Wasz-Hockert, T. Partanen, V. Vuorenkoski, E. Valanne,and K. Michelsson, Effect of training on ability to identifypreverbal vocalizations, Developmental Medicine and ChildNeurology, 6, 1964, 4.
  8. [8] O. Wasz-Hockert, T. Partanen, V. Vuorenkoski, E. Valanne,and K. Michelsson, The identification of some specific meaningsin infant vocalization, Experientia, 20, 1964, 154–156.
  9. [9] V.R. Fischelle and S. Karelitz, The cry latencies of normalinfants and those with brain damage, in C.F.Z. Boukydis andB.M. Lester (eds.), Infant crying (New York: Plenum Press,1985), 1–28.
  10. [10] P.F. Ostwald, D.G. Feedman, and J.H. Kurtz, Vocalizationsof infant twins, Folia Phoniatrica, 14, 1962, 37.
  11. [11] K. Michelsson and O. Michelsson, Phonation in the newborn,infant cry, International Journal of Pediatric Otorhinolaryn-gology, 49(1), 1999, S297–S301.
  12. [12] O.F. Reyes-Galaviz and C.A. Reyes-Garcia, A system for theprocessing of infant cry to recognize pathologies in recently bornbabies with neural networks, 9th Conf. Speech and Computer(SPECOM), Saint-Petersburg, Russia, 2004, 6.
  13. [13] J. Orozco and C.A. Reyes-Garcia, Detecting pathologies frominfant cry applying scaled conjugate gradient neural networks,Proc. ESANN, 2003, 249–354.
  14. [14] S. Zhou, L. Wu, X. Yuan, and W. Tan, Parameters selection ofSVM for function approximation based on differential evolution,Int. Conf. on Intelligent Systems and Knowledge Engineering(ISKE), 2007, 7.
  15. [15] V. Vapnik, An overview of statistical learning theory, IEEETransactions on Neural Networks, 5, 1999, 988–999.
  16. [16] W.C. Chan, K.C. Cheung, and C.J. Harris, On the modellingon nonlinear dynamic system using support vector neuralnetworks, Engineering Applications of Artificial Intelligence,14, 2001, 105–113.
  17. [17] G.Q. Zhu, S.R. Liu, and J.S. Yu, Support vector machine andits applications to function approximation, Journal of EastChina University of Science and Technology, 5, 2002, 555–559.
  18. [18] T.S. Furey, Support vector machine classification and validationof cancer tissue samples using microarray expression data,Journal of Bioinformatics, 16(10), 2000, 906–914.
  19. [19] S. Hongzong and W. Tao, Support vector machines classifi-cation for discriminating coronary heart disease patients fromnon-coronary heart disease, West Indian Medical Journal,56(5), 2007, 451–457.
  20. [20] J. Kan, W. Li, and K. Gao, Use of support vector machinesin recognition of fork branches of the standing trees, 2ndIEEE Conf. on Industrial Electronics and Applications, 2007,783–786.
  21. [21] O. Ivanciuc, Chapter 6: Applications of support vector machinesin chemistry, Computational chemistry, 23, (Hoboken, NJ,USA: Wiley-VCH, John Wiley & Sons, Inc., 2007).
  22. [22] M. Schmidt and H. Gish, Speaker identification via support vec-tor classifiers, International Conference on Acoustics, Speechand Signal Processing, ICASSP, 1, 1996, 105–108.
  23. [23] M.-H. Yang and B. Moghaddam, Gender classification usingsupport vector machines, International Conference on ImageProcessing, 2, 2000, 471–474.
  24. [24] J.Z. Shah and N. Salim, Neural networks and support vectormachines based bio-activity classification, 1st Int. Conf. onNatural Resources Engineering and Technology, 2006, 484–491.
  25. [25] C.A. Reyes-Garcia and S.E. Barajas-Montiel, Identifying painand hunger in infant cry with classifiers ensembles, Int.Conf. on Computational Intelligence for Modelling, Controland Automation and International Conference on IntelligentAgents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’05), 2005, 6.
  26. [26] Md. R. Hasan, M. Jamil, Md. G. Rabbani, and Md. S. Rahman,Speaker identification using Mel frequency cepstral coefficients,3rd Int. Conf. on Electrical and Computer Engineering ICECE2004, Dhaka, Bangladesh, 28–30 December 2004, 4.
  27. [27] J.Deller Jr., J. Hansen, and J. Proakis, Discrete-time processingof speech signals, 2nd ed. (New York, NY: IEEE Press, 2000).
  28. [28] F. Soong, E. Rosenberg, B. Juang, and L. Rabiner, A vectorquantization approach to speaker recognition, AT&T TechnicalJournal, 66, 1987, 14–26.
  29. [29] R. Sahak, W. Mansor, L.Y. Khuan, A. Zabidi, and F.Y.A.Rahman, An investigation into infant cry and Apgar scoreusing principal component analysis, 5th Signal Processing andIts Application Colloqium, 2008, 209–214.
  30. [30] Principal Component Analysis, unpublished note, http://www.support.sas.com/publishing/pubcat/chap5/55129.pdf.
  31. [31] S. Haykin, Neural networks: A comprehensive foundation, 2nded. (Canada: Pearson Education Asia, 1999).
  32. [32] W.J. Wang, Z.B. Xu, and W.Z. Lu, Determination of thespread parameter in the gaussian kernel for classification andregression, Neurocomputing, 55, 2003, 643–663.
  33. [33] Bertsekas, Dynamic programming and optimal control, Vols. Iand II (Belmont, MA: Athenas Scientific, 1995).
  34. [34] T. Zhou, J. Weng, X. Sun, and Z. Lu, Support vector ma-chines for classification of meiotic recombination hotspots andcoldspots in Saccharomyces Cerevisiae based on codon com-position, BMC Bioinformatics, 7(223), 2006, 8.
  35. [35] O.F. Reyes-Garcia (Online database), http://ingenieria.uatx.mx/∼orionfrg/cry/ (accessed Jul. 30, 2009).106

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