A review: survey on automatic infant cry analysis and classification

Automatic infant cry classification is one of the crucial studies under biomedical engineering scope, adopting the medical and engineering techniques for the classification of diverse physical and physiological conditions of the infants by their cry signal. Subsequently, plentiful studies have executed and issued, broadened the potential application of cry analyses. As yet, there is no ultimate literature documentation composed by performing a longitudinal study, emphasizing on the boast trend of automatic classification of infant cry. A review of literature is performed using the key words “infant cry” AND “automatic classification” from different online resources, regardless of the year of published in order to produce a comprehensive review. Review papers were excluded. Results of search reported about more than 300 papers and after some exclusion 101 papers were selected. This review endeavors at reporting an overview about recent advances and developments in the field of automated infant cry classification, specifically focusing on the developed infant cry databases and approaches involved in signal processing and recognition phases. Eventually, this article was accomplished with some possible implications which may lead for development of an advanced automated cry based classification systems for real time applications.

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[84]  I. M. Yassin,et al.  Optimization of MFCC parameters using Particle Swarm Optimization for diagnosis of infant hypothyroidism using Multi- Layer Perceptron , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

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[89]  Pilar Gómez-Gil,et al.  Type-2 Fuzzy Sets Applied to Pattern Matching for the Classification of Cries of Infants under Neurological Risk , 2009, ICIC.

[90]  Sergio Daniel Cano-Ortiz,et al.  A Radial Basis Function Network Oriented for Infant Cry Classification , 2004, CIARP.

[91]  Sazali Yaacob,et al.  Analysis of Infant Cry Through Weighted Linear Prediction Cepstral Coefficients and Probabilistic Neural Network , 2012, Journal of Medical Systems.

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[94]  Rohilah Sahak,et al.  Classification of Infant Cries with Asphyxia Using Multilayer Perceptron Neural Network , 2010, 2010 Second International Conference on Computer Engineering and Applications.

[95]  Carlos A. Reyes García,et al.  Identifying Pain and Hunger in Infant Cry with Classifiers Ensembles , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).