Optimal selection of mother wavelet for accurate infant cry classification View Full Text


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Article Info

DATE

2014-06

AUTHORS

J. Saraswathy, M. Hariharan, Thiyagar Nadarajaw, Wan Khairunizam, Sazali Yaacob

ABSTRACT

Wavelet theory is emerging as one of the prevalent tool in signal and image processing applications. However, the most suitable mother wavelet for these applications is still a relative question mark amongst researchers. Selection of best mother wavelet through parameterization leads to better findings for the analysis in comparison to random selection. The objective of this article is to compare the performance of the existing members of mother wavelets and to select the most suitable mother wavelet for accurate infant cry classification. Optimal wavelet is found using three different criteria namely the degree of similarity of mother wavelets, regularity of mother wavelets and accuracy of correct recognition during classification processes. Recorded normal and pathological infant cry signals are decomposed into five levels using wavelet packet transform. Energy and entropy features are extracted at different sub bands of cry signals and their effectiveness are tested with four supervised neural network architectures. Findings of this study expound that, the Finite impulse response based approximation of Meyer is the best wavelet candidate for accurate infant cry classification analysis. More... »

PAGES

439-456

References to SciGraph publications

  • 2013-06. Measurement of subcutaneous adipose tissue thickness by near-infrared in AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE
  • 2011. Genetic Fuzzy Relational Neural Network for Infant Cry Classification in PATTERN RECOGNITION
  • 2009. Qualitative and Quantitative Crying Analysis of New Born Babies Delivered Under High Risk Gestation in MULTIMODAL SIGNALS: COGNITIVE AND ALGORITHMIC ISSUES
  • 2008. Feature Extraction Based on Mel-Scaled Wavelet Packet Transform for the Diagnosis of Voice Disorders in 4TH KUALA LUMPUR INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING 2008
  • 2012-06. Analysis of Infant Cry Through Weighted Linear Prediction Cepstral Coefficients and Probabilistic Neural Network in JOURNAL OF MEDICAL SYSTEMS
  • 2013-06. Computer-aided diagnosis system for classifying benign and malignant thyroid nodules in multi-stained FNAB cytological images in AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE
  • 2008-10. Classification of cries of infants with cleft-palate using parallel hidden Markov models in MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
  • 2012-06. Predicting the continuous values of breast cancer relapse time by type-2 fuzzy logic system in AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE
  • 2012. Infant Cry Classification Using Genetic Selection of a Fuzzy Model in PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s13246-014-0264-y

    DOI

    http://dx.doi.org/10.1007/s13246-014-0264-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1007897570

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/24691930


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