Automatic classification of infant cry: A review

This paper reviews the some of significant works on infant cry signal analysis proposed in the past two decades and reviews the recent progress in this field. The cry of baby cannot be predicted accurately where it is very hard to identify for what it cries for. Experienced parents and specialists in the area of child care such as pediatrician and pediatric nurse can distinguish different sort of cries by just making use their individual perception on auditory sense. This is totally subjective evaluation and not suitable for clinical use. Non-invasive method has been widely used in infant cry signal analysis and has shown very promising results. Various feature extraction and classification algorithms used in infant cry analysis are briefly described. This review gives an insight on the current state of the art works in infant cry signal analysis and concludes with thoughts about the future directions for better representation and interpretation of infant cry signals.

[1]  Chakib Tadj,et al.  A Cry-Based Babies Identification System , 2010, ICISP.

[2]  Y. K. Lee,et al.  Orthogonal least square based support vector machine for the classification of infant cry with asphyxia , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.

[3]  Carlos A. Reyes García,et al.  A Fuzzy Relational Neural Network for Pattern Classification , 2004, CIARP.

[4]  Carlos A. Reyes García,et al.  Infant Cry Classification to Identify Hypoacoustics and Asphyxia with Neural Networks , 2004, MICAI.

[5]  Sandra E. Barajas-Montiel,et al.  Fuzzy Support Vector Machines for Automatic Infant Cry Recognition , 2006 .

[6]  Z. Benyo,et al.  Acoustic analysis of the infant cry: classical and new methods , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Sergio Daniel Cano-Ortiz,et al.  Evolutionary-Neural System to Classify Infant Cry Units for Pathologies Identification in Recently Born Babies , 2008, 2008 Seventh Mexican International Conference on Artificial Intelligence.

[8]  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.

[9]  Carlos A. Reyes-García,et al.  A Study on the Recognition of Patterns of Infant Cry for the Identification of Deafness in Just Born Babies with Neural Networks , 2003 .

[10]  Carlos A. Reyes García,et al.  Classification of Infant Crying to Identify Pathologies in Recently Born Babies with ANFIS , 2004, ICCHP.

[11]  Sazali Yaacob,et al.  Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network , 2012, Comput. Methods Programs Biomed..

[12]  Carlos A. Reyes García,et al.  A Study on the Recognition of Patterns of Infant Cry for the Identification of Deafness in Just Born Babies with Neural Networks , 2003, CIARP.

[13]  Hamed Sadjedi,et al.  Identification of hearing disorder by multi-band entropy cepstrum extraction from infant's cry , 2009, 2009 International Conference on Biomedical and Pharmaceutical Engineering.

[14]  G. Várallyay.  The melody of crying. , 2007, International journal of pediatric otorhinolaryngology.

[15]  Alfred S. Malowany,et al.  Classification of infant cry vocalizations using artificial neural networks (ANNs) , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[16]  Rohilah Sahak,et al.  Mel-frequency cepstrum coefficient analysis of infant cry with hypothyroidism , 2009, 2009 5th International Colloquium on Signal Processing & Its Applications.

[17]  Carlos A. Reyes García,et al.  Infant Cry Classification to Identify Hypo Acoustics and Asphyxia Comparing an Evolutionary-Neural System with a Neural Network System , 2005, MICAI.

[18]  A. S. Malowany,et al.  Identification of pain from infant cry vocalizations using artificial neural networks (ANNs) , 1995, SPIE Defense + Commercial Sensing.

[19]  Carlos A. Reyes García,et al.  Implementation of a Linguistic Fuzzy Relational Neural Network for Detecting Pathologies by Infant Cry Recognition , 2004, IBERAMIA.

[20]  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.

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

[22]  Dror Lederman,et al.  AUTOMATIC CLASSIFICATION OF THE CRY OF INFANTS WITH CLEFT PALATE , 2002 .

[23]  Sazali Yaacob,et al.  Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks , 2012, Expert Syst. Appl..

[24]  Tomomasa Nagashima,et al.  Iterative Forward Selection Method Based on Cross-validation Approach and Its Application to Infant Cry Classification , 2011, IMECS 2011.

[25]  Dror Lederman,et al.  Classification of cries of infants with cleft-palate using parallel hidden Markov models , 2008, Medical & Biological Engineering & Computing.

[26]  G. Várallyay,et al.  FUTURE PROSPECTS OF THE APPLICATION OF THE INFANT CRY IN THE MEDICINE , 2006 .

[27]  Sergio Daniel Cano-Ortiz,et al.  Rising shift of pitch frequency in the infant cry of some pathologic cases , 2001, MAVEBA.

[28]  Carlos A. Reyes García,et al.  Analysis of an Infant Cry Recognizer for the Early Identification of Pathologies , 2004, Summer School on Neural Networks.

[29]  J.O. Garcia,et al.  Mel-frequency cepstrum coefficients extraction from infant cry for classification of normal and pathological cry with feed-forward neural networks , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[30]  Dror Lederman,et al.  Estimation of Infants' Cry Fundamental Frequency using a Modified SIFT algorithm , 2010, ArXiv.

[31]  Rohilah Sahak,et al.  Binary Particle Swarm Optimization for selection of features in the recognition of infants cries with asphyxia , 2011, 2011 IEEE 7th International Colloquium on Signal Processing and its Applications.

[32]  L. Bocchi,et al.  A Robust Tool for Newborn Infant Cry Analysis , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[33]  I. M. Yassin,et al.  The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron Neural Network , 2010, 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[34]  A. Cohen,et al.  On the use of hidden Markov models in infants' cry classification , 2002, The 22nd Convention on Electrical and Electronics Engineers in Israel, 2002..

[36]  Masahiro Sawai,et al.  Statistical method for classifying cries of baby based on pattern recognition of power spectrum , 2010, Int. J. Biom..

[37]  Sazali Yaacob,et al.  Pathological infant cry analysis using wavelet packet transform and probabilistic neural network , 2011, Expert Syst. Appl..

[38]  Carlos A. Reyes García,et al.  Acoustic Features Analysis for Recognition of Normal and Hipoacusic Infant Cry Based on Neural Networks , 2009, IWANN.

[39]  Carlos A. Reyes García,et al.  Detecting Pathologies from Infant Cry Applying Scaled Conjugated Gradient Neural Networks , 2003, ESANN.

[40]  Sergio Daniel Cano-Ortiz,et al.  The spectral analysis of infant cry: an initial approximation , 1995, EUROSPEECH.

[41]  H. A. Patil,et al.  “Cry Baby”: Using Spectrographic Analysis to Assess Neonatal Health Status from an Infant’s Cry , 2010 .