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Automatic Recognition System of Infant Cry based on F-Transform

by Nemir Ahmed Al-azzawi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 102 - Number 12
Year of Publication: 2014
Authors: Nemir Ahmed Al-azzawi
10.5120/17869-8800

Nemir Ahmed Al-azzawi . Automatic Recognition System of Infant Cry based on F-Transform. International Journal of Computer Applications. 102, 12 ( September 2014), 28-32. DOI=10.5120/17869-8800

@article{ 10.5120/17869-8800,
author = { Nemir Ahmed Al-azzawi },
title = { Automatic Recognition System of Infant Cry based on F-Transform },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 102 },
number = { 12 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 28-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume102/number12/17869-8800/ },
doi = { 10.5120/17869-8800 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:32:57.839521+05:30
%A Nemir Ahmed Al-azzawi
%T Automatic Recognition System of Infant Cry based on F-Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 102
%N 12
%P 28-32
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A new approach has been proposed to design of an automatic infant cry recognition system based on the fuzzy transform (F-transform) that classifies two different kinds of cries, which come from physiological status and medical disease. Feature extraction and development of classification algorithms play an important role in the area of automatic analysis of infant cry signals. F-transform is the powerful tool for the approximation of continuous functions on a finite domain and has been used as feature extraction of infant cry signals. Neural networks is developed to classify the infant cry signals into physiological status and medical disease and trained with smoothing parameter to obtain better classification accuracy. Neural networks (recognition system) are trained from the random selection of crying babies. The experimental results show that the proposed give very promising classification accuracy of 96%. The proposed method can be used to assist medical investigation of the infant from the cry signals.

References
  1. R. E. Stark, et al. , "Features of infant sounds: The first eight weeks of life," Journal of Child Language, 2(02), pp. 205-221, 1975.
  2. M. P. Robb, et al. , "Cry analysis in infants resuscitated for apnea of infancy," International Journal of Pediatric Otorhinolaryngology, 71(7), pp. 1117-1123, 2007.
  3. B. M. Lester and C. F. Zachariah Boukydis. (1985). Infant Crying Theoretical and Research Perspectives [Online]. Boston, MA: Springer US. Available: http://dx. doi. org/10. 1007/978-1-4613-2381-5 MIT Access Only [Accessed].
  4. A. Cohen and E. Zmora, "Automatic classification of infants' hunger and pain cry," in Proc. Int. Conf. Digital Signal Processing, Cappellini, V. and Constantinides, AG, Eds. , Elsevier, Amsterdam, 1984, pp. 667-672.
  5. H. R. Gilbert and M. P. Robb, "Vocal fundamental frequency characteristics of infant hunger cries: birth to 12 months," International Journal of Pediatric Otorhinolaryngology, 34(3), pp. 237-243, 1996.
  6. L. Rautava, et al. , "Acoustic quality of cry in very-low-birth-weight infants at the age of 1 1/2 years," Early Human Development, 83(1), pp. 5-12, 2007.
  7. T. Etz, et al. , "Infant cry reliability: Acoustic homogeneity of spontaneous cries and pain-induced cries," Speech Communication, 58(0), pp. 91-100, 2014.
  8. A. K. Singh, et al. , "Classification of infant cries using epoch and spectral features," in Communications (NCC), 2013 National Conference on, 2013, pp. 1-5.
  9. S. Möller and R. Schönweiler, "Analysis of infant cries for the early detection of hearing impairment," Speech Communication, 28(3), pp. 175-193, 1999.
  10. D. N. John, "Review Neural circuits underlying crying and cry responding in mammals," 2013.
  11. L. Radhika Rani, et al. , "Estimation of Fundamental and Formant frequencies of infants ' cries; a study of Infants with congenital Heart disorder," 2013.
  12. P. Runefors, et al. , "Newborn infants' cry after heel?prick: analysis with sound spectrogram," Acta Paediatrica, 89(1), pp. 68-72, 2000.
  13. D. Lederman, et al. , "On the use of hidden Markov models in infants' cry classification," in Electrical and Electronics Engineers in Israel, 2002. The 22nd Convention of, 2002, pp. 350-352.
  14. S. E. Barajas-Montiel and C. A. Reyes-Garcia, "Identifying pain and hunger in infant cry with classifiers ensembles," in Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on, 2005, pp. 770-775.
  15. H. E. Baeck and M. N. de Souza, "Longitudinal study of the fundamental frequency of hunger cries along the first 6 months of healthy babies," Journal of Voice, 21(5), pp. 551-559, 2007.
  16. O. F. R. Galaviz and C. A. R. Garcia, "Infant Cry classification to identify hypoacoustics and asphyxia with neural networks," in MICAI 2004: Advances in Artificial Intelligence, ed: Springer, 2004, pp. 69-78.
  17. O. F. R. Galaviz and C. A. R. García, "Infant cry classification to identify hypo acoustics and asphyxia comparing an evolutionary-neural system with a neural network system," in MICAI 2005: Advances in Artificial Intelligence, ed: Springer, 2005, pp. 949-958.
  18. A. Zabidi, et al. , "Classification of Infant Cries with Asphyxia Using Multilayer Perceptron Neural Network," in Computer Engineering and Applications (ICCEA), 2010 Second International Conference on, 2010, pp. 204-208.
  19. A. Zabidi, et al. , "Detection of infant hypothyroidism with mel frequency cepstrum analysis and multi-layer perceptron classification," in Signal Processing and Its Applications (CSPA), 2010 6th International Colloquium on, 2010, pp. 1-5.
  20. M. Hariharan, et al. , "Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network," Journal of Medical Systems, 36(3), pp. 1309-1315, 2012.
  21. K. Michelsson and P. Sirviö, "Cry analysis in congenital hypothyroidism," Folia Phoniatrica et Logopaedica, 28(1), pp. 40-47, 1976.
  22. B. R. Vohr, et al. , "Abnormal brain-stem function (brain-stem auditory evoked response) correlates with acoustic cry features in term infants with hyperbilirubinemia," The Journal of Pediatrics, 115(2), pp. 303-308, 1989.
  23. D. Lederman, et al. , "Classification of cries of infants with cleft-palate using parallel hidden Markov models," Medical & biological engineering & computing, 46(10), pp. 965-975, 2008.
  24. Y. Okada, et al. , "Iterative forward on cross-validation approach and its application to infant cry classification," in International Multi Conference of Engineers and Computer Scientists, Hong Kong, 2011.
  25. G. Várallyay Jr, et al. , "Acoustic analysis of the infant cry: classical and new methods," in Engineering in Medicine and Biology Society, 2004. IEMBS'04. 26th Annual International Conference of the IEEE, 2004, pp. 313-316.
  26. K. Michelsson, et al. , "Crying in separated and non?separated newborns: sound spectrographic analysis," Acta Paediatrica, 85(4), pp. 471-475, 1996.
  27. R. Colton and A. Steinschneider, "The cry characteristics of an infant who died of the sudden infant death syndrome," Journal of Speech and Hearing Disorders, 46(4), pp. 359-363, 1981.
  28. Z. Tufekci, et al. , "Applied mel-frequency discrete wavelet coefficients and parallel model compensation for noise-robust speech recognition," Speech Communication, 48(10), pp. 1294-1307, 2006.
  29. 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.
  30. I. Perfilieva, "Fuzzy transforms: Theory and applications," Fuzzy Sets and Systems, 157(8), pp. 993-1023, 2006.
  31. I. Perfilieva, et al. , "Fuzzy transform in the analysis of data," International Journal of Approximate Reasoning, 48(1), pp. 36-46, 2008.
  32. I. Perfilieva, "Fuzzy transforms: A challenge to conventional transforms," in Advances in Images and Electron Physics. vol. 147, ed San Diego: Elsevier Academic Press, 2007, pp. 137-196.
  33. R. G. Barr, et al. , Crying as a sign, a symptom, & a signal : clinical, emotional, and developmental aspects of infant and toddler crying. London: Mac Keith Press : Distributed by Cambridge University Press, 2000.
  34. R. Rojas, Neural networks : a systematic introduction, 1996.
  35. W. W. Hager and H. Zhang, "A new conjugate gradient method with guaranteed descent and an efficient line search," SIAM Journal on Optimization, 16(1), pp. 170-192, 2005.
  36. C. A. R. GARCÍA. (2003). Detecting Pathologies from Infant Cry [Online]. Puebla, México: Instituto Nacional de Astrofisica Optica y Electronica (INAOE). Available: http://ccc. inaoep. mx/~kargaxxi/Archivos/onesecod/ [Accessed].
  37. N. Al-Azzawi, et al. , "Medical image fusion scheme using complex contourlet transform based on PCA," in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, Hilton Minneapolis, Minnesota, USA, 2009, pp. 5813-5816.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy-Transform (F-Transform) Infant cry Artificial neural network Automatic recognition Feature extraction.

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