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A Combined Classifier of Cry Units with New Acoustic Attributes

  • Sergio Cano
  • Israel Suaste
  • Daniel Escobedo
  • Carlos A. Reyes-García
  • Taco Ekkel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)

Abstract

The present work proposes a combined classifier of infant cry units that links in a single structure two focuses: a threshold-based classification and ANN-based classification. The threshold-based classifier considers 4 new acoustic features:stridor, melody, voicedness, shifts, that show properly their robustness in front of alterations of the acoustics of infant cry concerned with the presence of some diseases. In order to satisfy the automatic estimation their practical implementations are also considered. The ANN-based classifier consists in a feed-forward network using the method of Scale Gradient Conjugate (MSGC) as learning algorithm and the MFCCs as input vectors to the net. Each focus or classification stage gives in the exit one indicator (FN1 and FN2) that generates to the output a decision on two classes with gradation (normal, moderately-pathologic and pathologic). The results demonstrate the potentiality of these types of combined classifiers when the advantages of each focus in particular are properly emphasized

Keywords

Cry classification pattern recognition neural network 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sergio Cano
    • 1
  • Israel Suaste
    • 2
  • Daniel Escobedo
    • 1
  • Carlos A. Reyes-García
    • 2
  • Taco Ekkel
    • 3
  1. 1.CENPISUniversity of OrienteStgo de CubaCuba
  2. 2.INAOEPueblaMexico
  3. 3.Faculty of InformaticsUniversity of TwenteThe Netherlands

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