Skip to main content
SpringerLink
Log in

A review: survey on automatic infant cry analysis and classification

  • Review Paper
  • Published:
Health and Technology Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. UNICEF, WHO. The World Bank, United Nations. Levels and trends in child mortality: Report 2013. New York: UNICEF; 2013. www.childinfo.org/files/Child_Mortality_Report_2013.pdf Accessed 9 June 2012.

  2. WHO. Every newborn: An action plan to end preventable deaths. www.who.int/pmnch/media/events/2013/paper_item_7.pdf Accessed 9 June 2012.

  3. Orozco J, Garcia C. Detecting pathologies from infant cry applying scaled conjugate gradient neural networks. In: Proceedings of the European Symposium on Artificial Neural Networks, Bruges (Belgium), 2003: 349–354.

  4. Garcia JO, Reyes García CA. A study on the recognition of patterns of infant cry for the identification of deafness in just born babies with neural networks. Lect Notes Comput Sci. 2003;2905:342–9.

    Article  Google Scholar 

  5. Garcia JO, Reyes García CA. Acoustic features analysis for recognition of normal and hypoacoustic infant cry based on neural networks. Artificial Neural Nets Problem Solving Methods. 2003;2687:615–22.

    Article  Google Scholar 

  6. Garcia JO, Reyes Garcia CA. Mel frequency cepstrum coefficients extraction from infant cry for classification of normal and pathological cry with feed-forward neural networks. In: Proceedings of the International Joint Conference on Neural Networks, 2003: 3140–3145.

  7. Varallyay Jr G, Benyo Z, Illenyi A, Farkas Z, Kovacs L. Acoustic analysis of the infant cry: classical and new methods. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA, USA, 2004: 313–316.

  8. Barajas SE, Reyes CA. Your fuzzy relational neural network parameters optimization with a genetic algorithm. In: Proceedings of the 2005 IEEE International Conference on Fuzzy Systems, 2005: 684–689.

  9. Jam M, Sadjedi H. Identification of hearing disorder by multi-band entropy cepstrum extraction from infant’s cry. In: Proceedings of the 2009 IEEE International Conference on Biomedical and Pharmaceutical Engineering, 2009: pp 1–5.

  10. Reyes-Galaviz OF, Cano-Ortiz S, Reyes-Garcia C. Evolutionary-neural system to classify infant cry units for pathologies identification in recently born babies. In: Proceedings of the 8th Mexican International Conference on Artificial Intelligence (MICAI-2009), Guanajuato, Mexico, 2009: 330–335.

  11. Rosales-Perez A, Reyes-Garcia CA, Gomez-Gil P. Genetic fuzzy relational neural network for infant cry classification. Lect Notes Comput Sci. 2011;6718:288–96.

    Article  Google Scholar 

  12. Rosales-Perez A, Reyes-Garcia CA, Gonzalez JA, Arch-Tirado E. Infant cry classification using genetic selection of a fuzzy model. Lect Notes Comput Sci. 2012;7441:212–9.

    Article  Google Scholar 

  13. Hariharan M, Sindhu R, Yaacob S. Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network. Comput Methods Prog Biomed. 2012;108:559–69.

    Article  Google Scholar 

  14. Saraswathy J, Hariharan M, Vijean V, Yaacob S, Khairunizam W. Performance comparison of daubechies wavelet family in infant cry classification. In: Proceedings of the 2012 IEEE 8th International Colloquium on Signal Processing and its Applications, 2012: 451–455.

  15. Saraswathy J, Hariharan M, Nadarajaw T, Khairunizam W, Yaacob S. Optimal selection of mother wavelet for accurate infant cry classification. Australas Phys Eng Sci Med. 2014;37:439–56.

    Article  Google Scholar 

  16. Robb MP, Crowell DH, Dunn-Rankin P. Sudden infant death syndrome: cry characteristics. Int J Pediatr Otorhinolaryngol. 2013;77:1263–7.

    Article  Google Scholar 

  17. Lederman D, Cohen A, Zmora E, Wermke K, Hauschildt S, Stellzig-Eisenhauer A. On the use of hidden markov models in infants’ cry classification. In: Proceedings of the 22nd Convention in Electrical and Electronic Engineers in Israel, 2002: 350–352.

  18. Lederman D, Cohen A, Zmora E, Wermke K, Hauschildt S, Stellzig-Eisenhauer A. Classification of cries of infants with cleft-palate using parallel hidden markov models. Med Biol Eng Comput. 2008;46:965–75.

    Article  Google Scholar 

  19. Boukydis CFZ, Lester BM. Infant crying. Plenum press, 1995.

  20. Zabidi A, Mansor W, Khuan LY, Yassin IM, Sahak R. The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron neural network. In: Proceedings of the IEEE EMBS conference on biomedical engineering & sciences, Kuala Lumpur, Malaysia, 30th Nov-2nd Dec 2010, 2010: 126–129.

  21. Zabidi, Mansor W, Khuan LY, Yassin IM, Sahak R. Classification of infant cries with asphyxia using multilayer perceptron neural network. In: Proceedings of the 2nd International Conference on Computer Engineering and Applications, IEEE, 2010: 204–208.

  22. Sahak R, Mansor W, Lee YK, Yassin IM, Zabidi A. Orthogonal least square based support vector machine for the classification of infant cry with asphyxia. In: Proceedings of the 3rd international conference on biomedical engineering and informatics, IEEE, 2010: 986–990.

  23. Sahak R, Mansor W, Lee YK, Yassin IM, Zabidi A. Performance of Combined Support Vector Machine and Principal Component Analysis in Recognizing Infant Cry with Asphyxia. In: Proceedings of the 32nd Annual International Conference of the IEEE EMBS Buenos Aires, 2010: 6292–6295.

  24. Zabidi A, Mansor W, Khuan LY, Yassin IM, Sahak R. Binary particle swarm optimization for selection of features in the recognition of infants cries with asphyxia. In: Proceedings of the 7th International Colloquium on signal processing & its applications, 2011: 272–276.

  25. Hariharan M, Yaacob S, Awang SA. Pathological infant cry analysis using wavelet packet transform and probabilistic neural network. Exp Syst Appl. 2011;12:15377–82.

    Article  Google Scholar 

  26. Sahak R, Lee YK, Mansor W, Yassin IM, Zabidi A. Optimal features for classifying asphyxiated infant cry using support vector machine with RBF kernel. In: Proceedings of the 5th European IFMBE Conference, 2011: 98–101.

    Google Scholar 

  27. Mohd Ali MZ, Mansor W, Khuan LY, Zabidi A. Simulink model of Mel frequency cepstral coefficient analysis for extracting asphyxiated infant cry features. In: Proceedings of the 2012 International Conference on Biomedical Engineering (ICoBE), 2012: 475–477.

  28. Mohd Ali MZ, Mansor W, Khuan LY, Zabidi A. Asphyxiated infant cry classification using simulink model. In: Proceedings of the 2012 IEEE 8th International Colloquium on Signal Processing and its Applications, 2012; 491–494.

  29. Hariharan M, Saraswathy J, Sindhu R, Khairunizam W, Yaacob S. Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks. Exp Syst Appl. 2012;39:9515–23.

    Article  Google Scholar 

  30. Santiago-Sanchez K, Reyes-Garcia CA, Gomez-Gil P. Type-2 fuzzy sets applied to pattern matching for the classification of cries of infants under neurological risk. Lect Notes Comput Sci. 2009;5754:201–10.

    Article  Google Scholar 

  31. Kheddache Y, Tadj C. Acoustic measures of the cry characteristics of healthy newborns and newborns with pathologies. J Biomed Sci Eng. 2013;6:796–804.

    Article  Google Scholar 

  32. Orlandi S, Manfredi C, Bocchi1 L, Scattoni ML. Automatic newborn cry analysis: a non-invasive tool to help autism early diagnosis. In: Proceedings of the 34th Annual International Conference of the IEEE EMBS San Diego, California USA, 2012: 2953–2956.

  33. Sheinkopf SJ, Iverson JM, Rinaldi ML, Lester BM. A typical cry acoustics in 6-month-old infants at risk for autism spectrum disorder. INSAR Research Article, Autism Research, 2012:1–9.

  34. Zabidi A, Mansor W, Khuan LY, Sahak R, Rahman YA. Mel-frequency cepstrum coefficient analysis of infant cry with hypothyroidism. In: Proceedings of the5th International Colloquium on signal processing & its applications, 2009: 204–208.

  35. Zabidi A, Mansor W, Khuan LY, Yassin IM, Sahak R. Classification of infant cries with hypothyroidism using multilayer perceptron neural network. In: Proceedings of the 2009 IEEE International Conference on Signal and Image Processing Applications, 2009; 246–251.

  36. Zabidi A, Khuan LY, Mansor W, Yassin IM, Sahak R. Detection of infant hypothyroidism with mel frequency cepstrum analysis and multi-layerperceptron classification. In: Proceedings of the 6th International Colloquium on Signal Processing & Its Applications, 2010; 140–144.

  37. Zabidi A, Mansor W, Lee Yoot Khuan, Yassin IM, Sahak R. Investigation of mel frequency cepstrum coefficients parameters for classification of infant cries with hypothyroidism using MLP classifier. In: Proceedings of the 2010 International Joint Conference on Neural Networks, 2010: 1–4.

  38. Zabidi A, Mansor W, Khuan LY, Yassin IM, Sahak R. Optimization of MFCC parameters using particle swarm optimization for diagnosis of infant hypothyroidism using multi-layer perceptron. In: Proceedings of the 32nd Annual International Conference of the IEEE EMBS Buenos Aires, 2010: 1417–1420.

  39. Zabidi A, Mansor W, Khuan LY, Yassin IM, Sahak R. Discrete mutative particle swarm optimisation of mfcc computation for classifying hypothyroidal infant cry. In: Proceedings of the 2010 International Conference on Computer Applications and Industrial Electronics, 2010: 588–592.

  40. Lederman D. Estimation of infant’s cry fundamental frequency using a modified SIFT algorithm. arXiv: 1009.2796v1 [cs. SD], 2010: 1–5.

  41. Okada Y, Fukuta K, Nagashima T. Iterative forward on cross-validation approach and its application to infant cry classification. In: Proceedings of the International Multi Conference of Engineers and Computer Scientists, 2011.

  42. Alaie TC. Cry-based classification of healthy and sick infants using adapted boosting mixture learning method for gaussian mixture models. Modeling and Simulation in Engineering. 2012;5

  43. Alaie TC. Spitting of Gaussian models via adapted BML method pertaining to cry-based diagnostic system. Engineering. 2013;5:277–83.

    Article  Google Scholar 

  44. Kheddache Y, Tadj C. Newborn’s pathological identification system. In: Proceedings of the 11th International Conference on Information Science, Signal Processing and their Applications, 2012: 1024–1029.

  45. Kheddache T. Characterization of pathologic cries of newborns based on fundamental frequency estimation. Engineering. 2013;5:272–6.

    Article  Google Scholar 

  46. Kheddache T. Resonance frequencies behavior in pathologic cries of newborns. J Voice. 2014;29:1–12.

    Article  Google Scholar 

  47. Cano S, Suaste I, Escobedo D, Reyes-Garcia CA, Ekkel T. A radial basis function network oriented for infant cry classification. In Progress in Pattern Recognition, Image Analysis and Applications, 2004: 374–380.

  48. Patil HA. Infant identification from their cry. In: Proceedings of the 7th International Conference on Advances in Pattern Recognition; 2009. p. 107–10.

    Google Scholar 

  49. Messaound A, Tadj C. A cry-based babies identification system. Lect Notes Comput Sci. 2010;6134:192–9.

    Article  Google Scholar 

  50. Petroni M, Malowany AS, Johnston CC, Stevens BJ. Classification of infant cry vocalization using artificial neural networks (ANNs). In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing, 1995: 3475–3478.

  51. Petroni M, Malowany AS, Johnston CC, Stevens BJ. Identification of pain from infant cry vocalizations using artificial neural networks (ANNs). In: Proceedings of the SPIE's Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995: 729–738.

  52. Barajas-Montiel SE, Reyes-Garcia CA. Identifying pain and hunger in infant cry with classifiers ensembles. In: Proceedings of the International Conference on Computational Intelligence for Modeling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, 2005: 770–775.

  53. Barajas-Montiel SE, Reyes-Garcia CA, Arch-Tirado E, Mandujano M. Improving baby caring with automatic infant cry recognition. Lect Notes Comput Sci. 2006;4061:691–8.

    Article  Google Scholar 

  54. Abdulaziz Y, Ahmad SM. An accurate infant cry classification system based on continuos hidden markov model. In: Proceedings of the 2010 International Symposium in Information Technology, 2010: 1648–1652.

  55. Abdulaziz Y, Ahmad SM. Infant cry recognition system: A comparison of system performance based on Mel frequency and linear prediction cepstral coefficients. In: Proceedings of the International Conference on Information Retrieval & Knowledge Management, 2010: 260–263.

  56. Vempada RR, Kumar B, Rao KS. Characterization of infant cries using spectral and prosodic features. In: Proceedings of the National Conference in Communications, 2012: 1–5.

  57. Fort A, Ismaelli A, Manfredi C, Bruscaglioni P. Parametric and non-parametric estimation of speech formants: application to infant cry. Med Eng Phys. 1996;18:677–91.

    Article  Google Scholar 

  58. Lind K, Wermke K. Development of the vocal fundamental frequency of spontaneous cries during the first 3 months. Int J Pediatr Otorhinolaryngol. 2002;64:97–104.

    Article  Google Scholar 

  59. Benyo Z, Farkas Z, Illenyi A, Katona G, Varallyay Jr G. In formation transfer of sound signals. A case study: The infant cry. Is it a noise or an information? In: Proceedings of the 22rd International Congress and exposition on noise Control Engineering, 2004: 1–8.

  60. Manickam K, Li H. Complexity analysis of normal and deaf infant cry acoustic waves. In: Proceedings of the 4th International Workshop. Models and Analysis of Vocal Emissions for Biomedical Applications, 2005: 105–108.

  61. Baeck HE, de Souza MN. Longitudinal study of the fundamental frequency of hunger cries along the first 6 months of healthy babies. J Voice. 2007;21:551–9.

    Article  Google Scholar 

  62. Rautava L, Lempinen A, Ojala S, Parkkola R, Rikalainen H, Lapinleimu H, et al. Acoustic quality of cry in very-low-birth-weight infants at the age of 1 1/2 years. Early Hum Dev. 2007;83:5–12.

    Article  Google Scholar 

  63. Verduzco-Mendoza A, Arch-Tirado E, Reyes-Garcia CA, Leybon-Ibarra J, Licona-Bonilla J. Qualitative and quantitative crying analysis of new born babies delivered under high risk gestation. Multimodal Signals: Cognitive and Algorithmic Issues, 2009: 320–327.

  64. Wermke K, Robb MP. Fundamental frequency of neonatal crying: does body size matter? J Voice. 2010;24:388–94.

    Article  Google Scholar 

  65. Verduzco-Mendoza A, Arch-Tirado E, Reyes-Garcia CA, Leybon-Ibarra J, Licona-Bonilla J. Spectrographic infant cry analysis in newborns with profound hearing loss and perinatal high-risk newborns. Cir Cir. 2012;80:3–10.

    Google Scholar 

  66. Varallyay G. Future prospects of the infant cry in the medicine. Periodica Polytechnica SerElEng. 2006;50:47–62.

    Google Scholar 

  67. Reyes Galaviz OF, Reyes Garcia CA. Infant cry classification to identify hypo acoustics and asphyxia comparing an evolutionary-neural system with a neural network system. In: Proceedings of the MICAI: Advances in Artificial Intelligence, 2005: 949–958.

    Google Scholar 

  68. Reyes-Galaviz OF, Cano-Ortiz SD, Reyes-Garcia CA. Validation of the cry unit as primary element for cry analysis using an evolutionary-neural approach. In: Proceedings of the Mexican International Conference on Computer Science, 2008: 261–267.

  69. Mampe B, Friederici AD, Christophe A, Wermke K. Newborns’ cry melody is shaped by their native language. Curr Biol. 2009;19:1994–7.

    Article  Google Scholar 

  70. Daga RP, Panditrao AM. Acoustical analysis of pain cries’ in neonates: fundamental frequency. Special Issue of International Journal of Computer Applications on Electronics, Information and Communication Engineering. 2011;3:18–21.

    Google Scholar 

  71. Baeck HE, Souza MN. Study of acoustic features of newborn cries that correlate with the context. In: Proceedings of the IEEE 23rd Annual Conference in Engineering in Medicine and Biology Society, 2001: 2174–2177.

  72. Varallyay Jr G, Benyo Z, Illenyi A. The development of the melody of the infant cry to detect disorders during infancy. In: Proceedings of the IASTED International Conference on Biomedical Engineering, 2007; 14–16.

  73. Chittora A, Patil H. Data collection and corpus design for analysis of normal and pathological infant cry. In: Proceedings of the 2013 International Conference in Oriental COCOSDA held jointly with 2013 Conference on Asian Spoken Language Research and Evaluation, 2013: 1–6.

  74. Manfredi C, Bocchi L, Orlandi S, Spaccaterra L, Donzelli GP. High-resolution cry analysis in preterm newborn infants. Med Eng Phys. 2009;31:528–32.

    Article  Google Scholar 

  75. Sahak R, Mansor W, Khuan LY, Zabidi A, Yasmin F. An investigation into infant cry and apgar score using principle component analysis. In: Proceedings of the 2009 5th International Colloquium on Signal Processing & Its Applications, 2009: 209–214.

  76. Wermke K, Mende W, Manfredi C, Bruscaglioni P. Developmental aspects of infant’s cry melody and formants. Med Eng Phys. 2002;24:501–14.

    Article  Google Scholar 

  77. Mijovic B, Silva M, Van den BRH B, Allegaert K, Aerts JM, Berckmans D, et al. Assessment of pain expression in infant cry signals using empirical mode decomposition. Methods Inf Med. 2010;49:448–52.

    Article  Google Scholar 

  78. Abou-Abbas L, Montazeri L, Gargour C, Tadj C. On the use of EMD for automatic newborn cry segmentation. International Conference on Advances in Biomedical Engineering (ICABME), 2015, pp. 262–265.

  79. Chang C-Y, Li J-J. Application of Deep Learning for Recognizing Infant Cries. International Conference on Consumer Electronics-Taiwan. 2016.

  80. Ruiz Diaz MA, Reyes Garcia CA, Altamirano Robles LC, Xaltena Altamirano JE. Automatic infant cry analysis for the identification of qualitative features to help opportune diagnosis. Biomedical Signal Processing and Control. 2012;7:43–9.

    Article  Google Scholar 

  81. Michelsson K, Eklund K, Leepanen P, Lyytinen H. Cry characteristics of 172 healthy 1 to 7 day old infants. International Journal of Phoniatrics Speech Therapy and Communication Pathology. 2002;54:190–200.

    Google Scholar 

  82. Ruiz MA, Reyes CA, Altamirano LC. On the implementation of a method for automatic detection of infant cry units. Procedia Engineering. 2012;35:217–22.

    Article  Google Scholar 

  83. Varallyay G. Infant cry analyzer system for hearing disorder detection. Periodica Politechnica, Transaction on Automatic Control and Computer Science. 2004;49:1–4.

    Google Scholar 

  84. Fort A, Manfredi C. Acoustic analysis of newborn infant cry signals. Med Eng Phys. 1998;20:432–42.

    Article  Google Scholar 

  85. Amaro-Camargo E, Reyes Garcia CA. Applying statistical vectors of acoustic characteristics for the automatic classification of infant cry. In Advanced Intelligent Computing Theories and Applications, With Aspects of Theoretical and Methodological Issues, 2007: 1078–1085.

  86. Reyes-Galaviz OF, Reyes-Garcia CA. Fuzzy relational compression applied on feature vectors for infant cry recognition. In MICAI 2009: Advances in Artificial Intelligence. 2009;5845:420–31.

    Google Scholar 

  87. Jam MM, Sadjedi H. A system for detecting of infants with pain from normal infants based on multi band spectral entropy by infant’s cry analysis. In: Proceedings of the Second International Conference on Computer and Electrical Engineering, 2009: 72–76.

  88. Hariharan M, Chee LS, Yaacob S. Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network. J Med Syst. 2012;36:1309–15.

    Article  Google Scholar 

  89. Reyes-Galaviz OF, Reyes-Garcia CA. Infant cry classification to identify hypoacoustics and asphyxia with neural networks. In: Proceedings of 2004 MICAI: Advances in Artificial Intelligence, 2004: 69–78.

    Chapter  Google Scholar 

  90. Reyes-Galaviz OF, Verduzco A, Arch-Tirado E, Reyes-Garcia CA. Analysis of an infant cry recognizer for the early identification of pathologies. Nonlinear Speech Modeling and Applications. 2005;3445:404–9.

    Article  Google Scholar 

  91. Suaste-Rivas I, Reyes-Galviz OF, Diaz-Mendez A, Reyes Garcia CA. A fuzzy relational neural network for pattern classification. In CIARP. 2004;3287:358–65.

    Google Scholar 

  92. Suaste-Rivas I, Reyes-Galviz OF, Diaz-Mendez A, Reyes Garcia CA. Implementation of a linguistic fuzzy relational neural network for detecting pathologies by infant cry recognition. In IBERAMIA. 2004;3315:953–62.

    Google Scholar 

  93. Reyes-Galaviz OF, Arch Tirado E, Reyes Garcia CA. Classification of infant crying to identify pathologies in recently born babies with ANFIS. ICCHP Transaction, Lecture Notes in Artificial Intelligence. 2004;3118:408–15.

    Google Scholar 

  94. Barajas-Montiel SE, Reyes-Garcia CA. Fuzzy support vector machines for automatic infant cry recognition. In: Proceedings of the Intelligent Computing in Signal Processing and Pattern Recognition, 2006: 876–881.

  95. Suaste-Rivas I, Diaz-Mendez A, Reyes-Garcia C, Reyes-Galaviz O. hybrid neural network design and implementation on FPGA for infant cry recognition. In Text, Speech and Dialogue, 2006: 703–709.

  96. Messaoud A, Tadj C. Analysis of acoustic features of infant cry for classification purposes. In: Proceedings of the 24th Canadian Conference on Electrical and Computer Engineering, 2011: 89–92.

  97. Singh AK, Mukhopadhyay J, Rao SK. Classification of infant cries using source, system and supra-segmental feature. In: Proceedings of the 2013 Indian Conference on Medical Informatics and Telemedicine (ICMIT), 2013: pp 58–63.

  98. Cohen R, Lavner Y. Infant cry analysis and detection. In: Proceedings of the 27th Convention of Electrical and Electronic Engineers, 2012: 1–5.

  99. Saraswathy J, Hariharan M, Khairunizam W, Yaacob S, Thiyagar N. Infant Cry Classification: Time Frequency Analysis. In: Proceedings of the 2013 IEEE International Conference on Control System, Computing and Engineering, 2013: 499–504.

  100. Orlandi S, Reyes Garcia CA, Bandini A, Donzelli G, Manfredi C. Application of pattern recognition techniques to the classification of full-term and preterm infant cry. J Voice. 2015; https://doi.org/10.1016/j.jvoice.2015.08.007.

    Article  Google Scholar 

Download references

Acknowledgements

The Baby Chillanto Data Base is a property of the Instituto Nacional de Astrofisica Optica y Electronica – CONACYT, Mexico. We like to thank Dr. Carlos A. Reyes-Garcia, Dr. Emilio Arch-Tirado and his INR-Mexico group, and Dr. Edgar M. Garcia-Tamayo for their dedication of the collection of the Infant Cry data base. The authors would like to thank Dr. Carlos Alberto Reyes-Garcia, Researcher, CCC-Inaoep, Mexico for providing infant cry database. All authors declare that they have no financial or any commercial conflicts of interest. The work is supported by FRGS research grant [Grant No: 9003-00485] received from Ministry of Education Malaysia.

Author information

Authors and Affiliations

  1. School of Mechatronic Engineering, University Malaysia Perlis (UniMAP), Campus Pauh Putra, 02600, Perlis, Malaysia

    Saraswathy Jeyaraman, Wan Khairunizam & Shafriza Nisha

  2. Department of Electronics Engineering, National Institute of Technology, Srinagar (Garhwal), 246174, Uttarakhand, India

    Hariharan Muthusamy

  3. School of Bioprocess Engineering, University Malaysia Perlis (UniMAP), Campus Jejawi 3, 02600, Arau, Perlis, Malaysia

    Sarojini Jeyaraman

  4. Head of Department, Consultant Pediatrician & Adolescent Medicine Specialist, Department of Pediatrics, Hospital Sultanah Bahiyah, 05460, Alor Setar, Kedah, Malaysia

    Thiyagar Nadarajaw

  5. Universiti Kuala Lumpur Malaysian Spanish Institute, Kulim Hi-Tech Park, 09000, Kulim, Kedah, Malaysia

    Sazali Yaacob

Authors
  1. Saraswathy Jeyaraman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hariharan Muthusamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wan Khairunizam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sarojini Jeyaraman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Thiyagar Nadarajaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sazali Yaacob
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shafriza Nisha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saraswathy Jeyaraman.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jeyaraman, S., Muthusamy, H., Khairunizam, W. et al. A review: survey on automatic infant cry analysis and classification. Health Technol. 8, 391–404 (2018). https://doi.org/10.1007/s12553-018-0243-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12553-018-0243-5

Keywords

Search

Navigation