Third Degree Volterra Kernel for Newborn Cry Estimation

  • Gibran Etcheverry
  • Efraín López-Damian
  • Carlos A. Reyes-García
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)


Newborn cry analysis is a difficult task due to its nonstationary nature, combined to the presence of nonlinear behavior as well. Therefore, an adaptive hereditary optimization algorithm is implemented in order to avoid the use of windowing nor overlapping to capture the transient signal behavior. Identification of the linear part of this particular time series is carried out by employing an Autorregresive Moving Average (ARMA) structure; then, the resultant estimation error is approched by a Nonlinear Autorregresive Moving Average (NARMA) model, which realizes a Volterra cubic kernel by means of a bilinear homogeneous structure in order to capture burst behavior. Normal, deaf, asfixia, pain, and uncommon newborn cries are inspected for differentation.


Heart Rate Variability Speech Code Volterra Series Chaotic Time Series Volterra Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Golub, H., Corwin, M.: Infant cry: a clue to diagnosis. Pediatrics 69, 197–201 (1982)Google Scholar
  2. 2.
    Reyes-García, C., Cano-Ortiz, S.: Fundamentos Téoricos y Prácticos del Análisis de Llanto Infantil. Inaoe-Conacyt (2009)Google Scholar
  3. 3.
    Priestley, M.: Nonlinear and Nonstationary Time Series Analysis. Academic Press, London (1988)Google Scholar
  4. 4.
    Ezekiel, S., et al.: Seismic signal analysis using correlation dimension. In: Proc. in Applied Informatics (2003)Google Scholar
  5. 5.
    Lee, F.A., Nehorai, A.: Adaptive power spectrum estimation algorithm for heart rate variability analysis. Proc. of the IEEE, 273–276 (1992)Google Scholar
  6. 6.
    Karjalainen, P.: Estimation Theoretical Background of Root Tracking Algorithms with Applications to EEG. University of Kuopio Department of Applied Physics, Report Series (1996) ISSN 0788-4672Google Scholar
  7. 7.
    Potaminos, A., Narayanan, S.: A review of the acoustic and linguistic properties of children’s speech. In: IEEE 9th Workshop on Multimedia Signal Processing, MMSP, pp. 22–25 (2007)Google Scholar
  8. 8.
    Thyssen, J., Nielsen, H., Hansen, S.: Nonlinear short-term prediction in speech coding. IEEE Proceedings I, 185–188 (1994)Google Scholar
  9. 9.
    Alipoor, G., Savoji, M.: Speech coding using nonlinear prediction based on volterra series expansion. In: SPECOM, pp. 367–370 (2006)Google Scholar
  10. 10.
    Schnell, K., Lacroix, A.: Voiced excitation models for speech production based on time variable volterra systems. In: NOLISP, pp. 184–187 (2005)Google Scholar
  11. 11.
    Monin, A., Salut, G.: Arma lattice identification: A new hereditary algorithm. IEEE Trans. on Signal Processing 44(2), 360–370 (1996)CrossRefGoogle Scholar
  12. 12.
    Etcheverry, G., Suleiman, W., Monin, A.: Quadratic system identification by hereditary approach. In: ICASSP, vol. III, pp. 129–132 (2006)Google Scholar
  13. 13.
    Ljung, L.: System identification: theory for the user. Prentice-Hall, Englewood Cliffs (1999)CrossRefzbMATHGoogle Scholar
  14. 14.
    Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (1997)zbMATHGoogle Scholar
  15. 15.
    Rugh, J.: Nonlinear System Theory: The Volterra/Wiener Approach. Wiley, Chichester (1980)zbMATHGoogle Scholar
  16. 16.
    Li, C., Yu, J.: Volterra-tls method for chaotic time series prediction. Proc. of the IEEE, 48–51 (2008)Google Scholar
  17. 17.
    Wang, H., Gu, H.: Prediction of chaotic time series based on neural network with legendre polynomials. LNCS, vol. 5551, pp. 836–843. Springer, Heidelberg (2009)Google Scholar
  18. 18.
    Jirong, G., Xianwei, C., Jieming, Z.: An algorithm of predictions for chaotic time series based on volterra filter. In: ISECS, Proc. of the IEEE Computer Society, vol. 2, pp. 205–208 (2009)Google Scholar
  19. 19.
    Monin, A., Salut, G.: I.i.r volterra filtering with application to bilinear systems. IEEE Trans. on Signal Processing 44(9), 2209–2221 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Gibran Etcheverry
    • 1
  • Efraín López-Damian
    • 2
  • Carlos A. Reyes-García
    • 3
  1. 1.DIFUS-USONHermosilloMexico
  2. 2.Mechatronics DepartmentFIME-CIIDIT-UANLNuevo LeónMexico
  3. 3.Department of Computer ScienceINAOETonantzintlaMexico

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