Analyzing Infant Cries using a Committee of Neural Networks in Order to Detect Hypoxia Related Disorder

G.D. Magoulas (Editor), M. Poel, G. Dounias (Editor), T. Ekkel, D.A. Linkens (Editor)

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    7 Citations (Scopus)
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    Based on the hypothesis that the sound of the infant cry contains information on the infant's health status, research is done to improve classi¯cation of neonate crying sounds into categories called 'normal' and 'abnormal' - the latter referring to some hypoxia- related disorder. Research in this field is hindered by lack of test cases and limited understanding of feature relevance. The research described here combines various ways of dealing with the small data set problem. First, feature pre-selection is done using sequential backwards elimination of possible combinations where the performance of the set of features is tested by a Probabilistic Neural Network which has the advantage of fast learning. These features are then fed into a neural network committee consisting of Radial Basis Function Neural Networks. Bootstrapping is used to generate the committee. This construction yields a multi-classifier system with an overall classi¯cation performance of 85% on the data set, an increase of 34% with respect to the a priori probability of 51%. Several leave-1-out experiments for Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and Neural Networks (NN) have been conducted in order to compare the performance of the multi-classifier system. Keywords: Infant Cry Analysis; Feature reduction, Neural Networks; Support Vector Machines
    Original languageEnglish
    Pages (from-to)397-410
    Number of pages14
    JournalInternational journal on artificial intelligence tools
    Issue number3
    Publication statusPublished - Jun 2006


    • METIS-238672
    • IR-63414
    • EWI-6855


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