Infant Crying Classification by Using Genetic Algorithm and Artificial Neural Network
Abstract
Cry as the only way of communication of babies with the surrounding environment can be happened for many reasons such as diseases, suffocation, hunger, cold and heat feeling, pain and etc. So, the analyzing and detection of its source is very important for parents and health care providers. So the present study designed with the aim to test the performance of neural networks in the identification of the source of babies crying. Present study combines the genetic algorithm and artificial neural network with (Linear Predictive Coding) LPC and MFCC (Mel-Frequency Cepstral Coefficients) to classify the babies crying. The results of this study indicate the superiority of the proposed method compared to the other previous methods. This method could achieve the highest accuracy in the classification of newborns crying among the previous studies. Developing methods for classification audio signal analysis are promising and can be effectively applied in different areas such as babies crying.
1. Rosales-Pérez A, Reyes-García CA, Gonzalez JA, Reyes-Galaviz OF, Escalante HJ, Orlandi S. Classifying infant cry patterns by the Genetic Selection of a Fuzzy Model. Biomed Signal Process Control 2015;17:38-46.
2. Wasz-Höckert O, Partanen T, Vuorenkoski V, Michelsson K, Valanne E. The identification of some specific meanings in infant vocalization. Experientia 1964;20:154.
3. García JO, García CAR. Acoustic features analysis for recognition of normal and hypoacustic infant cry based on neural networks. International Work-Conference on Artificial Neural Networks. Berlin,Heidelberg: Springer, 2003.
4. Varallyay GJ, Benyó Z, Illényi A, Farkas Z, Kovács L. Acoustic analysis of the infant cry: classical and new methods. Engineering in Medicine and Biology Society. 2004. IEMBS'04. 26th Annual International Conference of the IEEE. USA: IEEE, 2004.
5. Ortiz SDC, Beceiro DIE, Ekkel T. A radial basis function network oriented for infant cry classification. Iberoamerican Congress on Pattern Recognition. Berlin, Heidelberg: Springer, 2004.
6. Hariharan M, Yaacob S, Awang SA. Pathological infant cry analysis using wavelet packet transform and probabilistic neural network. Expert Syst Appl 2011;38:15377-82.
7. Koolagudi SG, Rastogi D, Rao KS. Identification of language using mel-frequency cepstral coefficients (MFCC). Procedia Eng 2012;38:3391-8.
8. Hariharan M, Sindhu R, Yaacob S. Normal and hypoacoustic infant cry signal classification using time–frequency analysis and general regression neural network. Comput Methods Programs Biomed 2012;108:559-69.
9. Sheinkopf SJ, Iverson JM, Rinaldi ML, Lester BM. Atypical cry acoustics in 6‐month‐old infants at risk for autism spectrum disorder. Autism Res 2012;5:331-9.
10. Yom-Tov E, Inbar GF. Feature selection for the classification of movements from single movement-related potentials. IEEE Trans Neural Syst Rehabil Eng 2002;10:170-7.
11. Erguzel TT, Ozekes S, Tan O, Gultekin S. Feature selection and classification of electroencephalographic signals: an artificial neural network and genetic algorithm based approach. Clin EEG Neurosci 2015;46:321-326.
12. Bashiri A, Shahmoradi L, Beigy H, Savareh BA, Nosratabadi M, Kalhori SRN, et al. Quantitative EEG features selection in the classification of attention and response control in the children and adolescents with attention deficit hyperactivity disorder. Future Sci OA 2018;4:FSO292.
13. Alizadeh B, Safdari R, Zolnoori M, Bashiri A. Developing an intelligent system for diagnosis of asthma based on artificial neural network Acta Inform Med 2015;23:220.
14. Chan CF, Chui SP. Efficient codebook search procedure for vector-sum excited linear predictive coding of speech. Electron Lett 1994;30:1830-1.
15. Christiansen R, Rushforth C. Detecting and locating key words in continuous speech using linear predictive coding. IEEE Transactions on Acoustics, Speech, and Signal Processing. USA: IEEE, 1977.
16. Kazi RA, Prasad VMN, Kanagalingam J, Nutting CM, Clarke P, Rhys-Evans P, et al. Assessment of the formant frequencies in normal and laryngectomized individuals using linear predictive coding. J voice 2007;21:661-8.
17. Wu JD, Lin BF. Speaker identification based on the frame linear predictive coding spectrum technique. Expert Syst Appl 2009;36:8056-63.
18. Cutajar M, Gatt E, Grech I, Casha O, Micallef J. Comparative study of automatic speech recognition techniques. IET Signal Process 2013;7:25-46.
19. Fukada T, Tokuda K, Kobayashi T, Imai S. An adaptive algorithm for mel-cepstral analysis of speech. Acoustics, Speech, and Signal Processing. 1992. USA: IEEE, 1992.
20. Garcia JO, Garcia CR. Mel-frequency cepstrum coefficients extraction from infant cry for classification of normal and pathological cry with feed-forward neural networks. Neural Networks, 2003. Proceedings of the International Joint Conference. USA: IEEE, 2003.
21. De La Torre A, Peinado AM, Segura JC, Pérez-Córdoba JL, Benítez MC, Rubio AJ. Histogram equalization of speech representation for robust speech recognition. IEEE Trans Audio Speech Lang Process 2005;13,355-66.
22. Arsikere H, Lulich SM, Alwan A. Estimating speaker height and subglottal resonances using MFCCs and GMMs. IEEE Signal Process Lett 2014;21:159-62.
23. Holland JH. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. USA: MIT press, 1992.
24. Mohammadfam I, Soltanzadeh A, Moghimbeigi A, Savareh BA. Use of artificial neural networks (ANNs) for the analysis and modeling of factors that affect occupational injuries in large construction industries. Electron physician 2015;7;1515-22.
25. 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 Electrical and Electronics Engineers in Israel. USA: IEEE, 2002.
26. Lederman D, Zmora E, Hauschildt S, Stellzig-Eisenhauer A, Wermke K. Classification of cries of infants with cleft-palate using parallel hidden Markov models. Med Biol Eng Comput 2008;46:965-75.
27. Reyes-Galaviz OF, Tirado EA, Reyes-Garcia CA, Classification of infant crying to identify pathologies in recently born babies with ANFIS. International Conference on Computers for Handicapped Persons. Berlin, Heidelberg: Springer, 2004.
28. Jeyaraman S, Hariharan M, Khairunizam W, Jeyaraman S, Nadarajaw T, Yaacob S, et al. A review: survey on automatic infant cry analysis and classification. Health Technol 2018;8:391-404.
29. Suaste-Rivas I, Reyes-Galaviz OF, Diaz-Mendez A, Reyes-Garcia CA. A fuzzy relational neural network for pattern classification. Iberoamerican Congress on Pattern Recognition. Berlin, Heidelberg: Springer, 2004.
30. 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. Ibero-American Conference on Artificial Intelligence. Berlin, Heidelberg: Springer, 2004.
31. Barajas-Montiel SE, Reyes-García CA. Fuzzy support vector machines for automatic infant cry recognition. In: Huang DS, Li K, Irwin GW. Intelligent Computing in Signal Processing and Pattern Recognition. Berlin, Heidelberg: Springer, 2006.
32. Jam MM, Sadjedi H. A System for Detecting of Infants with Pain from Normal Infants Based on Multi-band Spectral Entropy by Infant. 2009 Second International Conference on Computer and Electrical Engineering. USA: IEEE, 2009.
33. Sahak R, Lee Y, Mansor W, Yassin A, Zabidi A, Optimized Support Vector Machine for classifying infant cries with asphyxia using Orthogonal Least Square. Computer Applications and Industrial Electronics (ICCAIE). USA: IEEE, 2010.
34. Zabidi A, Mansor W, Lee YK, Yassin IM, Sahak R. Binary particle swarm optimization for selection of features in the recognition of infants cries with asphyxia. Signal Processing and its Applications (CSPA). USA: IEEE, 2011.
35. G. Várallyay Jr. Infant cry analyzer system for hearing disorder detection. Spectrum 2004;18:20-1.
36. Srijiranon K, Eiamkanitchat N. Application of neuro-fuzzy approaches to recognition and classification of infant cry. TENCON 2014-2014 IEEE Region 10 Conference. USA: IEEE, 2014.
37. Orlandi S, Garcia CAR, Bandini A, Donzelli G, Manfredi C. Application of pattern recognition techniques to the classification of full-term and preterm infant cry. J Voice 2016;30:656-63.
38. Alaie HF, Abou-Abbas L, Tadj C. "Cry-based infant pathology classification using GMMs. Speech commun 2016;77:28-52.
39. Hariharan M, Sindhu R, Vijean V, Yazid H, Nadarajaw T, Yaacob S, et al. Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification. Comput Methods Programs Biomed 2018;155:39-51.
40. Lim WJ, Muthusamy H, Yazid H, Yaacob S, Nadarajaw T. Dual tree complex Wavelet Packet Transform based infant cry classification. AIP Conference Proceedings. USA: AIP Publishing, 2016.
41. Asthana S, Varma N, Mittal VK. An investigation into classification of infant cries using modified signal processing methods. 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), 2015. USA: IEEE, 2015
42. Hariharan M, Sindhu YCKR, Vijean V, Yazid H, Nadarajaw T, Polat K, et al. Higher Order Spectra based Features for Infant Cry Signal Classification. 한국감성과학회국제학술대회 (ICES) 2017;2017:54.
43. Díaz MAR, García CAR, Robles LCA,. Altamirano JEX, Mendoza AV. Automatic infant cry analysis for the identification of qualitative features to help opportune diagnosis. Biomed Signal Process Control 2012;7:43-9.
2. Wasz-Höckert O, Partanen T, Vuorenkoski V, Michelsson K, Valanne E. The identification of some specific meanings in infant vocalization. Experientia 1964;20:154.
3. García JO, García CAR. Acoustic features analysis for recognition of normal and hypoacustic infant cry based on neural networks. International Work-Conference on Artificial Neural Networks. Berlin,Heidelberg: Springer, 2003.
4. Varallyay GJ, Benyó Z, Illényi A, Farkas Z, Kovács L. Acoustic analysis of the infant cry: classical and new methods. Engineering in Medicine and Biology Society. 2004. IEMBS'04. 26th Annual International Conference of the IEEE. USA: IEEE, 2004.
5. Ortiz SDC, Beceiro DIE, Ekkel T. A radial basis function network oriented for infant cry classification. Iberoamerican Congress on Pattern Recognition. Berlin, Heidelberg: Springer, 2004.
6. Hariharan M, Yaacob S, Awang SA. Pathological infant cry analysis using wavelet packet transform and probabilistic neural network. Expert Syst Appl 2011;38:15377-82.
7. Koolagudi SG, Rastogi D, Rao KS. Identification of language using mel-frequency cepstral coefficients (MFCC). Procedia Eng 2012;38:3391-8.
8. Hariharan M, Sindhu R, Yaacob S. Normal and hypoacoustic infant cry signal classification using time–frequency analysis and general regression neural network. Comput Methods Programs Biomed 2012;108:559-69.
9. Sheinkopf SJ, Iverson JM, Rinaldi ML, Lester BM. Atypical cry acoustics in 6‐month‐old infants at risk for autism spectrum disorder. Autism Res 2012;5:331-9.
10. Yom-Tov E, Inbar GF. Feature selection for the classification of movements from single movement-related potentials. IEEE Trans Neural Syst Rehabil Eng 2002;10:170-7.
11. Erguzel TT, Ozekes S, Tan O, Gultekin S. Feature selection and classification of electroencephalographic signals: an artificial neural network and genetic algorithm based approach. Clin EEG Neurosci 2015;46:321-326.
12. Bashiri A, Shahmoradi L, Beigy H, Savareh BA, Nosratabadi M, Kalhori SRN, et al. Quantitative EEG features selection in the classification of attention and response control in the children and adolescents with attention deficit hyperactivity disorder. Future Sci OA 2018;4:FSO292.
13. Alizadeh B, Safdari R, Zolnoori M, Bashiri A. Developing an intelligent system for diagnosis of asthma based on artificial neural network Acta Inform Med 2015;23:220.
14. Chan CF, Chui SP. Efficient codebook search procedure for vector-sum excited linear predictive coding of speech. Electron Lett 1994;30:1830-1.
15. Christiansen R, Rushforth C. Detecting and locating key words in continuous speech using linear predictive coding. IEEE Transactions on Acoustics, Speech, and Signal Processing. USA: IEEE, 1977.
16. Kazi RA, Prasad VMN, Kanagalingam J, Nutting CM, Clarke P, Rhys-Evans P, et al. Assessment of the formant frequencies in normal and laryngectomized individuals using linear predictive coding. J voice 2007;21:661-8.
17. Wu JD, Lin BF. Speaker identification based on the frame linear predictive coding spectrum technique. Expert Syst Appl 2009;36:8056-63.
18. Cutajar M, Gatt E, Grech I, Casha O, Micallef J. Comparative study of automatic speech recognition techniques. IET Signal Process 2013;7:25-46.
19. Fukada T, Tokuda K, Kobayashi T, Imai S. An adaptive algorithm for mel-cepstral analysis of speech. Acoustics, Speech, and Signal Processing. 1992. USA: IEEE, 1992.
20. Garcia JO, Garcia CR. Mel-frequency cepstrum coefficients extraction from infant cry for classification of normal and pathological cry with feed-forward neural networks. Neural Networks, 2003. Proceedings of the International Joint Conference. USA: IEEE, 2003.
21. De La Torre A, Peinado AM, Segura JC, Pérez-Córdoba JL, Benítez MC, Rubio AJ. Histogram equalization of speech representation for robust speech recognition. IEEE Trans Audio Speech Lang Process 2005;13,355-66.
22. Arsikere H, Lulich SM, Alwan A. Estimating speaker height and subglottal resonances using MFCCs and GMMs. IEEE Signal Process Lett 2014;21:159-62.
23. Holland JH. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. USA: MIT press, 1992.
24. Mohammadfam I, Soltanzadeh A, Moghimbeigi A, Savareh BA. Use of artificial neural networks (ANNs) for the analysis and modeling of factors that affect occupational injuries in large construction industries. Electron physician 2015;7;1515-22.
25. 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 Electrical and Electronics Engineers in Israel. USA: IEEE, 2002.
26. Lederman D, Zmora E, Hauschildt S, Stellzig-Eisenhauer A, Wermke K. Classification of cries of infants with cleft-palate using parallel hidden Markov models. Med Biol Eng Comput 2008;46:965-75.
27. Reyes-Galaviz OF, Tirado EA, Reyes-Garcia CA, Classification of infant crying to identify pathologies in recently born babies with ANFIS. International Conference on Computers for Handicapped Persons. Berlin, Heidelberg: Springer, 2004.
28. Jeyaraman S, Hariharan M, Khairunizam W, Jeyaraman S, Nadarajaw T, Yaacob S, et al. A review: survey on automatic infant cry analysis and classification. Health Technol 2018;8:391-404.
29. Suaste-Rivas I, Reyes-Galaviz OF, Diaz-Mendez A, Reyes-Garcia CA. A fuzzy relational neural network for pattern classification. Iberoamerican Congress on Pattern Recognition. Berlin, Heidelberg: Springer, 2004.
30. 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. Ibero-American Conference on Artificial Intelligence. Berlin, Heidelberg: Springer, 2004.
31. Barajas-Montiel SE, Reyes-García CA. Fuzzy support vector machines for automatic infant cry recognition. In: Huang DS, Li K, Irwin GW. Intelligent Computing in Signal Processing and Pattern Recognition. Berlin, Heidelberg: Springer, 2006.
32. Jam MM, Sadjedi H. A System for Detecting of Infants with Pain from Normal Infants Based on Multi-band Spectral Entropy by Infant. 2009 Second International Conference on Computer and Electrical Engineering. USA: IEEE, 2009.
33. Sahak R, Lee Y, Mansor W, Yassin A, Zabidi A, Optimized Support Vector Machine for classifying infant cries with asphyxia using Orthogonal Least Square. Computer Applications and Industrial Electronics (ICCAIE). USA: IEEE, 2010.
34. Zabidi A, Mansor W, Lee YK, Yassin IM, Sahak R. Binary particle swarm optimization for selection of features in the recognition of infants cries with asphyxia. Signal Processing and its Applications (CSPA). USA: IEEE, 2011.
35. G. Várallyay Jr. Infant cry analyzer system for hearing disorder detection. Spectrum 2004;18:20-1.
36. Srijiranon K, Eiamkanitchat N. Application of neuro-fuzzy approaches to recognition and classification of infant cry. TENCON 2014-2014 IEEE Region 10 Conference. USA: IEEE, 2014.
37. Orlandi S, Garcia CAR, Bandini A, Donzelli G, Manfredi C. Application of pattern recognition techniques to the classification of full-term and preterm infant cry. J Voice 2016;30:656-63.
38. Alaie HF, Abou-Abbas L, Tadj C. "Cry-based infant pathology classification using GMMs. Speech commun 2016;77:28-52.
39. Hariharan M, Sindhu R, Vijean V, Yazid H, Nadarajaw T, Yaacob S, et al. Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification. Comput Methods Programs Biomed 2018;155:39-51.
40. Lim WJ, Muthusamy H, Yazid H, Yaacob S, Nadarajaw T. Dual tree complex Wavelet Packet Transform based infant cry classification. AIP Conference Proceedings. USA: AIP Publishing, 2016.
41. Asthana S, Varma N, Mittal VK. An investigation into classification of infant cries using modified signal processing methods. 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), 2015. USA: IEEE, 2015
42. Hariharan M, Sindhu YCKR, Vijean V, Yazid H, Nadarajaw T, Polat K, et al. Higher Order Spectra based Features for Infant Cry Signal Classification. 한국감성과학회국제학술대회 (ICES) 2017;2017:54.
43. Díaz MAR, García CAR, Robles LCA,. Altamirano JEX, Mendoza AV. Automatic infant cry analysis for the identification of qualitative features to help opportune diagnosis. Biomed Signal Process Control 2012;7:43-9.
| Files | ||
| Issue | Vol 58, No 10 (2020) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/acta.v58i10.4916 | |
| Keywords | ||
| Crying Mel-Frequency Cepstral Coefficients Linear Predictor Coefficients Neural Networks Genetic Algorithms | ||
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How to Cite
1.
Bashiri A, Hosseinkhani R. Infant Crying Classification by Using Genetic Algorithm and Artificial Neural Network. Acta Med Iran. 2020;58(10):531-539.
