{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T16:17:28Z","timestamp":1778948248397,"version":"3.51.4"},"reference-count":102,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,2,5]],"date-time":"2021-02-05T00:00:00Z","timestamp":1612483200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,2,5]],"date-time":"2021-02-05T00:00:00Z","timestamp":1612483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100008545","name":"Georgia State University","doi-asserted-by":"crossref","award":["IN-1213813"],"award-info":[{"award-number":["IN-1213813"]}],"id":[{"id":"10.13039\/100008545","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J AUDIO SPEECH MUSIC PROC."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper reviews recent research works in infant cry signal analysis and classification tasks. A broad range of literatures are reviewed mainly from the aspects of data acquisition, cross domain signal processing techniques, and machine learning classification methods. We introduce pre-processing approaches and describe a diversity of features such as MFCC, spectrogram, and fundamental frequency, etc. Both acoustic features and prosodic features extracted from different domains can discriminate frame-based signals from one another and can be used to train machine learning classifiers. Together with traditional machine learning classifiers such as KNN, SVM, and GMM, newly developed neural network architectures such as CNN and RNN are applied in infant cry research. We present some significant experimental results on pathological cry identification, cry reason classification, and cry sound detection with some typical databases. This survey systematically studies the previous research in all relevant areas of infant cry and provides an insight on the current cutting-edge works in infant cry signal analysis and classification. We also propose future research directions in data processing, feature extraction, and neural network classification fields to better understand, interpret, and process infant cry signals.<\/jats:p>","DOI":"10.1186\/s13636-021-00197-5","type":"journal-article","created":{"date-parts":[[2021,2,5]],"date-time":"2021-02-05T12:09:02Z","timestamp":1612526942000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":109,"title":["A review of infant cry analysis and classification"],"prefix":"10.1186","volume":"2021","author":[{"given":"Chunyan","family":"Ji","sequence":"first","affiliation":[]},{"given":"Thosini Bamunu","family":"Mudiyanselage","sequence":"additional","affiliation":[]},{"given":"Yutong","family":"Gao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2766-3096","authenticated-orcid":false,"given":"Yi","family":"Pan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,5]]},"reference":[{"issue":"3","key":"197_CR1","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/BF02150709","volume":"20","author":"O. Wasz-H\u00f6ckert","year":"1964","unstructured":"O. Wasz-H\u00f6ckert, T. J. Partanen, V. Vuorenkoski, K. Michelsson, E. Valanne, The identification of some specific meanings in infant vocalization. Experientia. 20(3), 154 (1964).","journal-title":"Experientia"},{"key":"197_CR2","doi-asserted-by":"publisher","unstructured":"J. Mukhopadhyay, B. Saha, B. Majumdar, A. K. Majumdar, S. Gorain, B. K. Arya, S. D. Bhattacharya, A. Singh, in 2013 Indian Conference on Medical Informatics and Telemedicine, ICMIT 2013. An evaluation of human perception for neonatal cry using a database of cry and underlying cause, (2013). https:\/\/doi.org\/10.1109\/IndianCMIT.2013.6529410.","DOI":"10.1109\/IndianCMIT.2013.6529410"},{"key":"197_CR3","doi-asserted-by":"publisher","unstructured":"J. Saraswathy, M. Hariharan, S. Yaacob, W. Khairunizam, in 2012 International Conference on Biomedical Engineering (ICoBE). Automatic classification of infant cry: a review, (2012), pp. 543\u2013548. https:\/\/doi.org\/10.1109\/ICoBE.2012.6179077.","DOI":"10.1109\/ICoBE.2012.6179077"},{"key":"197_CR4","doi-asserted-by":"publisher","unstructured":"L. Floridi, AI and its new winter: from myths to realities. Philos. Technol., 1\u20133 (2020). https:\/\/doi.org\/10.1007\/s13347-020-00396-6.","DOI":"10.1007\/s13347-020-00396-6"},{"key":"197_CR5","doi-asserted-by":"publisher","unstructured":"A. A. Dixit, N. V. Dharwadkar, in Proceedings of the 2018 IEEE International Conference on Communication and Signal Processing, ICCSP 2018. A survey on detection of reasons behind infant cry using speech processing, (2018), pp. 190\u2013194. https:\/\/doi.org\/10.1109\/ICCSP.2018.8524517.","DOI":"10.1109\/ICCSP.2018.8524517"},{"key":"197_CR6","doi-asserted-by":"publisher","unstructured":"G. Zamzmi, R. Kasturi, D. Goldgof, R. Zhi, T. Ashmeade, Y. Sun, A review of automated pain assessment in infants: features, classification tasks, and databases (2018). https:\/\/doi.org\/10.1109\/RBME.2017.2777907.","DOI":"10.1109\/RBME.2017.2777907"},{"key":"197_CR7","doi-asserted-by":"publisher","unstructured":"O. F. Reyes-Galaviz, E. A. Tirado, C. A. Reyes-Garcia, in International Conference on Computers for Handicapped Persons, 3118. Classification of infant crying to identify pathologies in recently born babies with ANFIS, (2004), pp. 408\u2013415. https:\/\/doi.org\/10.1007\/978-3-540-27817-7_60.","DOI":"10.1007\/978-3-540-27817-7_60"},{"key":"197_CR8","doi-asserted-by":"publisher","unstructured":"O. F. Reyes-Galaviz, S. D. Cano-Ortiz, C. A. Reyes-Garc\u00eda, in 7th Mexican International Conference on Artificial Intelligence - Proceedings of the Special Session, MICAI 2008. Evolutionary-neural system to classify infant cry units for pathologies identification in recently born babies, (2008), pp. 330\u2013335. https:\/\/doi.org\/10.1109\/MICAI.2008.73.","DOI":"10.1109\/MICAI.2008.73"},{"key":"197_CR9","doi-asserted-by":"publisher","unstructured":"E. Franti, I. Ispas, M. Dascalu, in 2018 41st International Conference on Telecommunications and Signal Processing, TSP 2018. Testing the Universal Baby Language hypothesis - automatic infant speech recognition with CNNs, (2018), pp. 1\u20134. https:\/\/doi.org\/10.1109\/TSP.2018.8441412.","DOI":"10.1109\/TSP.2018.8441412"},{"key":"197_CR10","unstructured":"GitHub - gveres\/donateacry-corpus: an infant cry audio corpus that\u2019s being built through the Donate-a-cry campaign - see http:\/\/donateacry.com. https:\/\/github.com\/gveres\/donateacry-corpus. Accessed 07 Aug 2020."},{"key":"197_CR11","doi-asserted-by":"publisher","first-page":"51982","DOI":"10.1109\/ACCESS.2019.2911427","volume":"7","author":"M. Severini","year":"2019","unstructured":"M. Severini, D. Ferretti, E. Principi, S. Squartini, Automatic detection of cry sounds in neonatal intensive care units by using deep learning and acoustic scene simulation. IEEE Access. 7:, 51982\u201351993 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2911427.","journal-title":"IEEE Access"},{"key":"197_CR12","doi-asserted-by":"publisher","unstructured":"X. Zhang, Y. Zou, Y. Liu, in Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). AICDS: an infant crying detection system based on lightweight convolutional neural network, (2018). https:\/\/doi.org\/10.1007\/978-3-319-94361-9_14.","DOI":"10.1007\/978-3-319-94361-9_14"},{"key":"197_CR13","doi-asserted-by":"publisher","unstructured":"L. Liu, Y. Li, K. Kuo, in 2018 International Conference on Information and Computer Technologies, ICICT 2018. Infant cry signal detection, pattern extraction and recognition, (2018), pp. 159\u2013163. https:\/\/doi.org\/10.1109\/INFOCT.2018.8356861.","DOI":"10.1109\/INFOCT.2018.8356861"},{"key":"197_CR14","doi-asserted-by":"publisher","unstructured":"S. Sharma, P. R. Myakala, R. Nalumachu, S. V. Gangashetty, V. K. Mittal, in 2017 7th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2017. Acoustic analysis of infant cry signal towards automatic detection of the cause of crying, (2018), pp. 117\u2013122. https:\/\/doi.org\/10.1109\/ACIIW.2017.8272600.","DOI":"10.1109\/ACIIW.2017.8272600"},{"key":"197_CR15","doi-asserted-by":"publisher","unstructured":"C. Ji, X. Xiao, S. Basodi, Y. Pan, in Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and So. Deep learning for asphyxiated infant cry classification based on acoustic features and weighted prosodic features, (2019). https:\/\/doi.org\/10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00206.","DOI":"10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00206"},{"key":"197_CR16","doi-asserted-by":"publisher","unstructured":"G. Gu, X. Shen, P. Xu, in Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018. A set of DSP system to detect baby crying, (2018), pp. 411\u2013415. https:\/\/doi.org\/10.1109\/IMCEC.2018.8469246.","DOI":"10.1109\/IMCEC.2018.8469246"},{"key":"197_CR17","doi-asserted-by":"publisher","unstructured":"Y. Lavner, R. Cohen, D. Ruinskiy, H. Ijzerman, in 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016. Baby cry detection in domestic environment using deep learning, (2017). https:\/\/doi.org\/10.1109\/ICSEE.2016.7806117.","DOI":"10.1109\/ICSEE.2016.7806117"},{"key":"197_CR18","doi-asserted-by":"publisher","unstructured":"D. Ferretti, M. Severini, E. Principi, A. Cenci, S. Squartini, in 2018 26th European Signal Processing Conference (EUSIPCO). Infant cry detection in adverse acoustic environments by using deep neural networks, (2018), pp. 992\u2013996. https:\/\/doi.org\/10.23919\/EUSIPCO.2018.8553135.","DOI":"10.23919\/EUSIPCO.2018.8553135"},{"key":"197_CR19","doi-asserted-by":"publisher","unstructured":"A. Chittora, H. A. Patil, in International Conference on Text, Speech, and Dialogue, 9302. Significance of unvoiced segments and fundamental frequency in infant cry analysis, (2015), pp. 273\u2013281. https:\/\/doi.org\/10.1007\/978-3-319-24033-6_31.","DOI":"10.1007\/978-3-319-24033-6_31"},{"key":"197_CR20","doi-asserted-by":"publisher","unstructured":"S. Bano, K. M. Ravikumar, in Proceedings of the IEEE International Conference on Soft-Computing and Network Security, ICSNS 2015. Decoding baby talk: a novel approach for normal infant cry signal classification, (2015), pp. 24\u201326. https:\/\/doi.org\/10.1109\/ICSNS.2015.7292392.","DOI":"10.1109\/ICSNS.2015.7292392"},{"issue":"6","key":"197_CR21","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1016\/j.jvoice.2015.08.007","volume":"30","author":"S. Orlandi","year":"2016","unstructured":"S. Orlandi, C. A. Reyes Garcia, A. Bandini, G. Donzelli, C. Manfredi, Application of pattern recognition techniques to the classification of full-term and preterm infant cry. J. Voice. 30(6), 656\u2013663 (2016). https:\/\/doi.org\/10.1016\/j.jvoice.2015.08.007.","journal-title":"J. Voice"},{"issue":"12","key":"197_CR22","first-page":"1379","volume":"5","author":"M. V. Varsharani Bhagatpatil","year":"2014","unstructured":"M. V. Varsharani Bhagatpatil, An automatic infant\u2019s cry detection using linear frequency cepstrum coefficients (LFCC). Int. J. Sci. Eng. Res.5(12), 1379\u20131383 (2014).","journal-title":"Int. J. Sci. Eng. Res."},{"key":"197_CR23","doi-asserted-by":"publisher","unstructured":"S. Yamamoto, Y. Yoshitomi, M. Tabuse, K. Kushida, T. Asada, Recognition of a baby\u2019s emotional cry towards robotics baby caregiver. Int. J. Adv. Robot. Syst.10: (2013). https:\/\/doi.org\/10.5772\/55406.","DOI":"10.5772\/55406"},{"key":"197_CR24","doi-asserted-by":"publisher","unstructured":"A. K. Singh, J. Mukhopadhyay, K. S. Rao, in 2013 Indian Conference on Medical Informatics and Telemedicine, ICMIT 2013. Classification of infant cries using source, system and supra-segmental features, (2013), pp. 58\u201363. https:\/\/doi.org\/10.1109\/IndianCMIT.2013.6529409.","DOI":"10.1109\/IndianCMIT.2013.6529409"},{"key":"197_CR25","doi-asserted-by":"publisher","unstructured":"K. Manikanta, K. P. Soman, M. Sabarimalai Manikandan, in 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), 4. Deep learning based effective baby crying recognition method under indoor background sound environments, (2019), pp. 1\u20136. https:\/\/doi.org\/10.1109\/CSITSS47250.2019.9031058.","DOI":"10.1109\/CSITSS47250.2019.9031058"},{"key":"197_CR26","doi-asserted-by":"publisher","unstructured":"G. Joshi, C. Dandvate, H. Tiwari, A. Mundhare, in Proceedings - 2017 International Conference on Vision, Image and Signal Processing, ICVISP 2017. Prediction of probability of crying of a child and system formation for cry detection and financial viability of the system, (2017), pp. 134\u2013141. https:\/\/doi.org\/10.1109\/ICVISP.2017.33.","DOI":"10.1109\/ICVISP.2017.33"},{"key":"197_CR27","doi-asserted-by":"publisher","unstructured":"R. Torres, D. Battaglino, L. Lepauloux, in International Conference on Engineering Applications of Neural Networks. Baby cry sound detection: a comparison of hand crafted features and deep learning approach, (2017). https:\/\/doi.org\/10.1007\/978-3-319-65172-9_15.","DOI":"10.1007\/978-3-319-65172-9_15"},{"key":"197_CR28","doi-asserted-by":"publisher","unstructured":"M. Moharir, M. U. Sachin, R. Nagaraj, M. Samiksha, S. Rao, Identification of asphyxia in newborns using GPU for deep learning, (2017). https:\/\/doi.org\/10.1109\/I2CT.2017.8226127.","DOI":"10.1109\/I2CT.2017.8226127"},{"key":"197_CR29","unstructured":"C. C. Onu, I. Udeogu, E. Ndiomu, U. Kengni, D. Precup, G. M. Sant\u2019anna, E. Alikor, P. Opara, Ubenwa: cry-based diagnosis of birth asphyxia. Nips:, 2\u20135 (2017). https:\/\/doi.org\/1711.06405."},{"key":"197_CR30","doi-asserted-by":"publisher","first-page":"3","DOI":"10.17485\/ijst\/2017\/v10i3\/110617","volume":"10","author":"M. U. Sachin","year":"2017","unstructured":"M. U. Sachin, R. Nagaraj, M. Samiksha, S. Rao, M. Moharir, GPU based deep learning to detect asphyxia in neonates. Indian J. Sci. Technol.10:, 3 (2017). https:\/\/doi.org\/10.17485\/ijst\/2017\/v10i3\/110617.","journal-title":"Indian J. Sci. Technol."},{"key":"197_CR31","doi-asserted-by":"publisher","unstructured":"O. M. Badreldine, N. A. Elbeheiry, A. N. M. Haroon, S. Elshehaby, E. M. Marzook, in ICENCO 2018 - 14th International Computer Engineering Conference: Secure Smart Societies. Automatic diagnosis of asphyxia infant cry signals using wavelet based mel frequency cepstrum features, (2019), pp. 96\u2013100. https:\/\/doi.org\/10.1109\/ICENCO.2018.8636151.","DOI":"10.1109\/ICENCO.2018.8636151"},{"key":"197_CR32","doi-asserted-by":"publisher","unstructured":"H. B. Sailor, H. A. Patil, in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. Auditory filterbank learning using ConvRBM for infant cry classification, (2018), pp. 706\u2013710. https:\/\/doi.org\/10.21437\/Interspeech.2018-1536.","DOI":"10.21437\/Interspeech.2018-1536"},{"key":"197_CR33","doi-asserted-by":"publisher","unstructured":"J. Saraswathy, M. Hariharan, V. Vijean, S. Yaacob, W. Khairunizam, in Proceedings - 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, CSPA 2012. Performance comparison of Daubechies wavelet family in infant cry classification, (2012), pp. 451\u2013455. https:\/\/doi.org\/10.1109\/CSPA.2012.6194767.","DOI":"10.1109\/CSPA.2012.6194767"},{"issue":"3","key":"197_CR34","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1007\/s10916-010-9591-z","volume":"36","author":"M. Hariharan","year":"2012","unstructured":"M. Hariharan, L. S. Chee, S. Yaacob, Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network. J. Med. Syst.36(3), 1309\u20131315 (2012). https:\/\/doi.org\/10.1007\/s10916-010-9591-z.","journal-title":"J. Med. Syst."},{"key":"197_CR35","doi-asserted-by":"publisher","unstructured":"L. Le, A. N. M. H. Kabir, C. Ji, S. Basodi, Y. Pan, in Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems Workshops, MASSW 2019. Using transfer learning, SVM, and ensemble classification to classify baby cries based on their spectrogram images, (2019). https:\/\/doi.org\/10.1109\/MASSW.2019.00028.","DOI":"10.1109\/MASSW.2019.00028"},{"issue":"1","key":"197_CR36","doi-asserted-by":"publisher","first-page":"012019","DOI":"10.1088\/1742-6596\/1528\/1\/012019","volume":"1528","author":"T. Nadia Maghfira","year":"2020","unstructured":"T. Nadia Maghfira, T. Basaruddin, A. Krisnadhi, Infant cry classification using CNN - RNN. J. Phys. Conf. Ser.1528(1), 012019 (2020). https:\/\/doi.org\/10.1088\/1742-6596\/1528\/1\/012019.","journal-title":"J. Phys. Conf. Ser."},{"key":"197_CR37","doi-asserted-by":"publisher","unstructured":"S. P. Dewi, A. L. Prasasti, B. Irawan, in Proceedings - 2019 IEEE International Conference on Signals and Systems, ICSigSys 2019. The study of baby crying analysis using MFCC and LFCC in different classification methods, (2019), pp. 18\u201323. https:\/\/doi.org\/10.1109\/ICSIGSYS.2019.8811070.","DOI":"10.1109\/ICSIGSYS.2019.8811070"},{"key":"197_CR38","doi-asserted-by":"publisher","unstructured":"I. A. Banica, H. Cucu, A. Buzo, D. Burileanu, C. Burileanu, in 2016 International Conference on Communications (COMM). Automatic methods for infant cry classification, (2016), pp. 51\u201354. https:\/\/doi.org\/10.1109\/ICComm.2016.7528261.","DOI":"10.1109\/ICComm.2016.7528261"},{"key":"197_CR39","doi-asserted-by":"publisher","unstructured":"K. Sharma, C. Gupta, S. Gupta, in 2019 10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019. Infant weeping calls decoder using statistical feature extraction and Gaussian mixture models, (2019), pp. 1\u20136. https:\/\/doi.org\/10.1109\/ICCCNT45670.2019.8944527.","DOI":"10.1109\/ICCCNT45670.2019.8944527"},{"key":"197_CR40","doi-asserted-by":"publisher","unstructured":"M. Huckvale, in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. Neural network architecture that combines temporal and summative features for infant cry classification in the Interspeech 2018 Computational Paralinguistics Challenge, (2018), pp. 137\u2013141. https:\/\/doi.org\/10.21437\/Interspeech.2018-1959.","DOI":"10.21437\/Interspeech.2018-1959"},{"key":"197_CR41","doi-asserted-by":"publisher","unstructured":"M. A. Tugtekin Turan, E. Erzin, in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. Monitoring infant\u2019s emotional cry in domestic environments using the capsule network architecture, (2018). https:\/\/doi.org\/10.21437\/Interspeech.2018-2187.","DOI":"10.21437\/Interspeech.2018-2187"},{"key":"197_CR42","doi-asserted-by":"publisher","unstructured":"B. W. Schuller, S. Steidl, A. Batliner, P. B. Marschik, H. Baumeister, F. Dong, S. Hantke, F. B. Pokorny, E. M. Rathner, K. D. Bartl-Pokorny, C. Einspieler, D. Zhang, A. Baird, S. Amiriparian, K. Qian, Z. Ren, M. Schmitt, P. Tzirakis, S. Zafeiriou, in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. The INTERSPEECH 2018 computational paralinguistics challenge: atypical & self-assessed affect, crying & heart beats, (2018), pp. 122\u2013126. https:\/\/doi.org\/10.21437\/Interspeech.2018-51.","DOI":"10.21437\/Interspeech.2018-51"},{"key":"197_CR43","doi-asserted-by":"publisher","unstructured":"G. Z. Felipe, R. L. Aguiat, Y. M. G. Costa, C. N. Silla, S. Brahnam, L. Nanni, S. McMurtrey, in 2019 International Conference on Systems, Signals and Image Processing (IWSSIP). Identification of infants\u2019 cry motivation using spectrograms, (2019), pp. 181\u2013186. https:\/\/doi.org\/10.1109\/IWSSIP.2019.8787318.","DOI":"10.1109\/IWSSIP.2019.8787318"},{"issue":"3","key":"197_CR44","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1038\/s41390-019-0592-4","volume":"87","author":"J. J. Parga","year":"2020","unstructured":"J. J. Parga, S. Lewin, J. Lewis, D. Montoya-Williams, A. Alwan, B. Shaul, C. Han, S. Y. Bookheimer, S. Eyer, M. Dapretto, L. Zeltzer, L. Dunlap, U. Nookala, D. Sun, B. H. Dang, A. E. Anderson, Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?Pediatr. Res.87(3), 576\u2013580 (2020). https:\/\/doi.org\/10.1038\/s41390-019-0592-4.","journal-title":"Pediatr. Res."},{"key":"197_CR45","doi-asserted-by":"publisher","unstructured":"R. I. Tuduce, M. S. Rusu, H. Cucu, C. Burileanu, in 2019 42nd International Conference on Telecommunications and Signal Processing, TSP 2019. Automated baby cry classification on a hospital-acquired baby cry database, (2019), pp. 343\u2013346. https:\/\/doi.org\/10.1109\/TSP.2019.8769075.","DOI":"10.1109\/TSP.2019.8769075"},{"key":"197_CR46","doi-asserted-by":"publisher","unstructured":"M. S. Rusu, t. S. Diaconescu, G. Sardescu, E. Brtil, in 2015 International Conference on Speech Technology and Human-Computer Dialogue, SpeD 2015. Database and system design for data collection of crying related to infant\u2019s needs and diseases, (2015). https:\/\/doi.org\/10.1109\/SPED.2015.7343077.","DOI":"10.1109\/SPED.2015.7343077"},{"key":"197_CR47","doi-asserted-by":"publisher","unstructured":"I. A. Banica, H. Cucu, A. Buzo, D. Burileanu, C. Burileanu, in 2016 39th International Conference on Telecommunications and Signal Processing, TSP 2016. Baby cry recognition in real-world conditions, (2016), pp. 315\u2013318. https:\/\/doi.org\/10.1109\/TSP.2016.7760887.","DOI":"10.1109\/TSP.2016.7760887"},{"key":"197_CR48","doi-asserted-by":"publisher","unstructured":"C. Y. Chang, L. Y. Tsai, in Workshops of the International Conference on Advanced Information Networking and Applications. A CNN-based method for infant cry detection and recognition, (2019). https:\/\/doi.org\/10.1007\/978-3-030-15035-8_76.","DOI":"10.1007\/978-3-030-15035-8_76"},{"issue":"3","key":"197_CR49","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1109\/JAS.2019.1911435","volume":"6","author":"L. Liu","year":"2019","unstructured":"L. Liu, W. Li, X. Wu, B. X. Zhou, Infant cry language analysis and recognition: an experimental approach. IEEE\/CAA J. Autom. Sin.6(3), 778\u2013788 (2019). https:\/\/doi.org\/10.1109\/JAS.2019.1911435.","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"197_CR50","doi-asserted-by":"publisher","unstructured":"C. Y. Chang, J. J. Li, in 2016 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2016. Application of deep learning for recognizing infant cries, (2016), pp. 1\u20132. https:\/\/doi.org\/10.1109\/ICCE-TW.2016.7520947.","DOI":"10.1109\/ICCE-TW.2016.7520947"},{"key":"197_CR51","doi-asserted-by":"publisher","unstructured":"K. Wu, C. Zhang, X. Wu, D. Wu, X. Niu, in Proceedings - 2019 34rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2019. Research on acoustic feature extraction of crying for early screening of children with autism, (2019), pp. 290\u2013295. https:\/\/doi.org\/10.1109\/YAC.2019.8787725.","DOI":"10.1109\/YAC.2019.8787725"},{"key":"197_CR52","doi-asserted-by":"publisher","unstructured":"A. Zabidi, L. Y. Khuan, W. Mansor, I. M. Yassin, R. Sahak, in Proceedings - CSPA 2010: 2010 6th International Colloquium on Signal Processing and Its Applications. Detection of infant hypothyroidism with mel frequency cepstrum analysis and multi-layer perceptron classification, (2010), pp. 140\u2013144. https:\/\/doi.org\/10.1109\/CSPA.2010.5545331.","DOI":"10.1109\/CSPA.2010.5545331"},{"key":"197_CR53","doi-asserted-by":"publisher","unstructured":"A. Zabidi, W. Mansor, L. Y. Khuan, I. M. Yassin, R. Sahak, in 2009 IEEE International Conference on Signal and Image Processing Applications. Classification of infant cries with hypothyroidism using multilayer perceptron neural network, (2009), pp. 246\u2013251. https:\/\/doi.org\/10.1109\/ICSIPA.2009.5478608.","DOI":"10.1109\/ICSIPA.2009.5478608"},{"key":"197_CR54","unstructured":"Y. Okada, K. Fukuta, T. Nagashima, in IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011, 1. Iterative forward selection method based on cross-validation approach and its application to infant cry classification, (2011), pp. 49\u201352."},{"issue":"2","key":"197_CR55","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1504\/IJBM.2010.031791","volume":"2","author":"X. Wang","year":"2010","unstructured":"X. Wang, T. Nagashima, K. Fukuta, Y. Okada, M. Sawai, H. Tanaka, T. Uozumi, Statistical method for classifying cries of baby based on pattern recognition of power spectrum. Int. J. Biom.2(2), 113\u2013123 (2010). https:\/\/doi.org\/10.1504\/IJBM.2010.031791.","journal-title":"Int. J. Biom."},{"key":"197_CR56","doi-asserted-by":"publisher","unstructured":"C. Pan, W. Zhao, S. Deng, W. Wei, Y. Zhang, Y. Xu, in Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). The methods of realizing baby crying recognition and intelligent monitoring based on DNN-GMM-HMM, (2018), pp. 352\u2013356. https:\/\/doi.org\/10.1109\/IMCEC.2018.8469328.","DOI":"10.1109\/IMCEC.2018.8469328"},{"key":"197_CR57","doi-asserted-by":"publisher","unstructured":"R. Cohen, Y. Lavner, in 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel. Infant cry analysis and detection, (2012), pp. 1\u20135. https:\/\/doi.org\/10.1109\/EEEI.2012.6376996.","DOI":"10.1109\/EEEI.2012.6376996"},{"key":"197_CR58","doi-asserted-by":"publisher","first-page":"107020","DOI":"10.1016\/j.apacoust.2019.107020","volume":"158","author":"G. Sharma","year":"2020","unstructured":"G. Sharma, K. Umapathy, S. Krishnan, Trends in audio signal feature extraction methods. Appl. Acoust.158:, 107020 (2020). https:\/\/doi.org\/10.1016\/j.apacoust.2019.107020.","journal-title":"Appl. Acoust."},{"key":"197_CR59","doi-asserted-by":"publisher","unstructured":"F. Al\u00edas, J. C. Socor\u00f3, X. Sevillano, A review of physical and perceptual feature extraction techniques for speech, music and environmental sounds. Appl. Sci.6(5) (2016). https:\/\/doi.org\/10.3390\/app6050143.","DOI":"10.3390\/app6050143"},{"issue":"1","key":"197_CR60","first-page":"768","volume":"4","author":"A. Zabidi","year":"2017","unstructured":"A. Zabidi, I. M. Yassin, H. A. Hassan, N. Ismail, M. M. A. M. Hamzah, Z. I. Rizman, H. Z. Abidin, Detection of asphyxia in infants using deep learning ction of asphyxia in infants using deep learning convolutional neural network (CNN) trained on Mel frequency cepstrum coefficient (MFCC) features. Aust. Ranger Bull.4(1), 768\u2013778 (2017).","journal-title":"Aust. Ranger Bull."},{"key":"197_CR61","doi-asserted-by":"publisher","unstructured":"A. Zabidi, W. Mansor, Y. K. Lee, I. M. Yassin, R. Sahak, in Proceedings - 2011 IEEE 7th International Colloquium on Signal Processing and Its Applications. Binary particle swarm optimization for selection of features in the recognition of infants cries with asphyxia, (2011), pp. 272\u2013276. https:\/\/doi.org\/10.1109\/CSPA.2011.5759886.","DOI":"10.1109\/CSPA.2011.5759886"},{"key":"197_CR62","doi-asserted-by":"publisher","unstructured":"M. Z. M. Ali, W. Mansor, Y. K. Lee, A. Zabidi, in Proceedings - 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, 10. Asphyxiated infant cry classification using Simulink model, (2012), pp. 491\u2013494. https:\/\/doi.org\/10.1109\/CSPA.2012.6194778.","DOI":"10.1109\/CSPA.2012.6194778"},{"key":"197_CR63","doi-asserted-by":"publisher","unstructured":"A. Zabidi, L. Y. Khuan, W. Mansor, I. M. Yassin, R. Sahak, in 2010 2nd International Conference on Computer Engineering and Applications, 1. Classification of infant cries with asphyxia using multilayer perceptron neural network, (2010), pp. 204\u2013208. https:\/\/doi.org\/10.1109\/ICCEA.2010.47.","DOI":"10.1109\/ICCEA.2010.47"},{"key":"197_CR64","doi-asserted-by":"publisher","unstructured":"S. P. Dewi, A. L. Prasasti, B. Irawan, in Proceedings - 2019 IEEE International Conference on Internet of Things and Intelligence System, IoTaIS 2019. Analysis of LFCC feature extraction in baby crying classification using KNN, (2019), pp. 86\u201391. https:\/\/doi.org\/10.1109\/IoTaIS47347.2019.8980389.","DOI":"10.1109\/IoTaIS47347.2019.8980389"},{"key":"197_CR65","doi-asserted-by":"publisher","unstructured":"S. S. Jagtap, P. K. Kadbe, P. N. Arotale. System propose for Be acquainted with newborn cry emotion using linear frequency cepstral coefficient, (2016), pp. 238\u2013242. https:\/\/doi.org\/10.1109\/ICEEOT.2016.7755094.","DOI":"10.1109\/ICEEOT.2016.7755094"},{"key":"197_CR66","doi-asserted-by":"publisher","unstructured":"M. Kia, S. Kia, N. Davoudi, R. Biniazan, in 2nd International Conference on Innovative Computing Technology, INTECH 2012. A detection system of infant cry using fuzzy classification including dialing alarm calls function, (2012), pp. 224\u2013229. https:\/\/doi.org\/10.1109\/INTECH.2012.6457776.","DOI":"10.1109\/INTECH.2012.6457776"},{"key":"197_CR67","doi-asserted-by":"publisher","unstructured":"A. Osmani, M. Hamidi, A. Chibani, in Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI. Machine learning approach for infant cry interpretation, (2018). https:\/\/doi.org\/10.1109\/ICTAI.2017.00038.","DOI":"10.1109\/ICTAI.2017.00038"},{"key":"197_CR68","unstructured":"Praat: doing phonetics by computer. https:\/\/www.fon.hum.uva.nl\/praat\/. Accessed 07 Aug 2020."},{"key":"197_CR69","doi-asserted-by":"publisher","unstructured":"C. Ji, S. Basodi, X. Xiao, Y. Pan, in International Conference on AI and Mobile Services. Infant sound classification on multi-stage CNNs with hybrid features and prior knowledge, (2020). https:\/\/doi.org\/10.1007\/978-3-030-59605-7_1.","DOI":"10.1007\/978-3-030-59605-7_1"},{"key":"197_CR70","doi-asserted-by":"publisher","unstructured":"Y. D. Rosita, H. Junaedi, in Proceedings - 2016 2nd International Conference on Science and Technology-Computer, ICST 2016. Infant\u2019s cry sound classification using Mel-Frequency Cepstrum Coefficients feature extraction and Backpropagation Neural Network, (2017). https:\/\/doi.org\/10.1109\/ICSTC.2016.7877367.","DOI":"10.1109\/ICSTC.2016.7877367"},{"key":"197_CR71","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.bspc.2014.10.002","volume":"17","author":"A. Rosales-P\u00e9rez","year":"2015","unstructured":"A. Rosales-P\u00e9rez, C. A. Reyes-Garc\u00eda, J. A. Gonzalez, O. F. Reyes-Galaviz, H. J. Escalante, S. Orlandi, Classifying infant cry patterns by the Genetic Selection of a Fuzzy Model. Biomed. Signal Process. Control. 17:, 38\u201346 (2015). https:\/\/doi.org\/10.1016\/j.bspc.2014.10.002.","journal-title":"Biomed. Signal Process. Control"},{"issue":"12","key":"197_CR72","doi-asserted-by":"publisher","first-page":"15377","DOI":"10.1016\/j.eswa.2011.06.025","volume":"38","author":"M. Hariharan","year":"2011","unstructured":"M. Hariharan, S. Yaacob, S. A. Awang, Pathological infant cry analysis using wavelet packet transform and probabilistic neural network. Expert Syst. Appl.38(12), 15377\u201315382 (2011). https:\/\/doi.org\/10.1016\/j.eswa.2011.06.025.","journal-title":"Expert Syst. Appl."},{"key":"197_CR73","doi-asserted-by":"publisher","unstructured":"S. Tejaswini, N. Sriraam, G. C. M. Pradeep, in 2016 International Conference on Circuits, Controls, Communications and Computing. Recognition of infant cries using wavelet derived mel frequency feature with SVM classification, (2017). https:\/\/doi.org\/10.1109\/CIMCA.2016.8053313.","DOI":"10.1109\/CIMCA.2016.8053313"},{"key":"197_CR74","doi-asserted-by":"publisher","unstructured":"B. McFee, C. Raffel, D. Liang, D. Ellis, M. McVicar, E. Battenberg, O. Nieto, in Proceedings of the 14th Python in Science Conference. librosa: audio and music signal analysis in Python, (2015). https:\/\/doi.org\/10.25080\/majora-7b98e3ed-003.","DOI":"10.25080\/majora-7b98e3ed-003"},{"key":"197_CR75","doi-asserted-by":"publisher","unstructured":"F. Eyben, M. W\u00f6llmer, B. Schuller, in Proceedings of the 18th ACM international conference on Multimedia. OpenSMILE - the Munich versatile and fast open-source audio feature extractor, (2010). https:\/\/doi.org\/10.1145\/1873951.1874246.","DOI":"10.1145\/1873951.1874246"},{"issue":"9","key":"197_CR76","first-page":"45","volume":"8","author":"N. S. A. Wahid","year":"2016","unstructured":"N. S. A. Wahid, P. Saad, M. Hariharan, Automatic infant cry pattern classification for a multiclass problem. J. Telecommun. Electron. Comput. Eng.8(9), 45\u201352 (2016).","journal-title":"J. Telecommun. Electron. Comput. Eng."},{"key":"197_CR77","doi-asserted-by":"publisher","unstructured":"C. Y. Chang, Y. C. Hsiao, S. T. Chen, in Proceedings - 2015 18th International Conference on Network-Based Information Systems, NBiS 2015. Application of incremental SVM learning for infant cries recognition, (2015), pp. 607\u2013610. https:\/\/doi.org\/10.1109\/NBiS.2015.90.","DOI":"10.1109\/NBiS.2015.90"},{"key":"197_CR78","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.specom.2015.12.001","volume":"77","author":"H. Farsaie Alaie","year":"2016","unstructured":"H. Farsaie Alaie, L. Abou-Abbas, C. Tadj, Cry-based infant pathology classification using GMMs. Speech Commun.77:, 28\u201352 (2016). https:\/\/doi.org\/10.1016\/j.specom.2015.12.001.","journal-title":"Speech Commun."},{"key":"197_CR79","doi-asserted-by":"publisher","unstructured":"H. Liu, J. Li, Y. Q. Zhang, Y. Pan, in Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel\/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Network, 2005. An adaptive genetic fuzzy multi-path routing protocol for wireless ad-hoc networks, (2005), pp. 468\u2013475. https:\/\/doi.org\/10.1109\/SNPD-SAWN.2005.12.","DOI":"10.1109\/SNPD-SAWN.2005.12"},{"key":"197_CR80","doi-asserted-by":"publisher","unstructured":"K. Santiago-S\u00e1nchez, C. A. Reyes-Garc\u00eda, P. G\u00f3mez-Gil, in International Conference on Intelligent Computing. Type-2 fuzzy sets applied to pattern matching for the classification of cries of infants under neurological risk, (2009), pp. 201\u2013210. https:\/\/doi.org\/10.1007\/978-3-642-04070-2_23.","DOI":"10.1007\/978-3-642-04070-2_23"},{"key":"197_CR81","doi-asserted-by":"publisher","unstructured":"S. F. Molaeezadeh, M. Salarian, M. H. Moradi, in The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012). Type-2 fuzzy pattern matching for classifying hunger and pain cries of healthy full-term infants, (2012), pp. 233\u2013237. https:\/\/doi.org\/10.1109\/AISP.2012.6313750.","DOI":"10.1109\/AISP.2012.6313750"},{"issue":"5","key":"197_CR82","doi-asserted-by":"publisher","first-page":"358","DOI":"10.17743\/jaes.2015.0025","volume":"63","author":"S. Ntalampiras","year":"2015","unstructured":"S. Ntalampiras, Audio pattern recognition of baby crying sound events. J. Audio Eng. Soc.63(5), 358\u2013369 (2015). https:\/\/doi.org\/10.17743\/jaes.2015.0025.","journal-title":"J. Audio Eng. Soc."},{"key":"197_CR83","doi-asserted-by":"publisher","unstructured":"R. I. Tuduce, H. Cucu, C. Burileanu, in 2018 41st International Conference on Telecommunications and Signal Processing, TSP 2018. Why is my baby crying? An in-depth analysis of paralinguistic features and classical machine learning algorithms for baby cry classification, (2018), pp. 1\u20134. https:\/\/doi.org\/10.1109\/TSP.2018.8441363.","DOI":"10.1109\/TSP.2018.8441363"},{"key":"197_CR84","doi-asserted-by":"publisher","unstructured":"R. Robu, F. Feier, V. Stoicu-Tivadar, C. Ilie, I. En\u0103tescu, in 2011 15th IEEE International Conference on Intelligent Engineering Systems. The analysis of the new-borns\u2019 cry using NEONAT and data mining techniques, (2011), pp. 235\u2013238. https:\/\/doi.org\/10.1109\/INES.2011.5954750.","DOI":"10.1109\/INES.2011.5954750"},{"key":"197_CR85","doi-asserted-by":"publisher","unstructured":"M. Petroni, A. S. Malowany, C. C. Johnston, B. J. Stevens, in IEEE International Conference on Acoustics, Speech and Signal Processing, 5. Classification of infant cry vocalizations using artificial neural networks (ANNs), (1995), pp. 3475\u20133478. https:\/\/doi.org\/10.1109\/icassp.1995.479734.","DOI":"10.1109\/icassp.1995.479734"},{"issue":"10","key":"197_CR86","doi-asserted-by":"publisher","first-page":"9515","DOI":"10.1016\/j.eswa.2012.02.102","volume":"39","author":"M. Hariharan","year":"2012","unstructured":"M. Hariharan, J. Saraswathy, R. Sindhu, W. Khairunizam, S. Yaacob, Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks. Expert Syst. Appl.39(10), 9515\u20139523 (2012). https:\/\/doi.org\/10.1016\/j.eswa.2012.02.102.","journal-title":"Expert Syst. Appl."},{"key":"197_CR87","unstructured":"H. Lim, J. Park, K. Lee, Y. Han, in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop. Rare sound event detection using 1D convolutional recurrent neural networks, (2017), pp. 80\u201384."},{"key":"197_CR88","doi-asserted-by":"publisher","unstructured":"K. Srijiranon, N. Eiamkanitchat, in IEEE Region 10 Annual International Conference, Proceedings\/TENCON. Application of neuro-fuzzy approaches to recognition and classification of infant cry, (2015), pp. 1\u20136. https:\/\/doi.org\/10.1109\/TENCON.2014.7022296.","DOI":"10.1109\/TENCON.2014.7022296"},{"key":"197_CR89","unstructured":"S. Sabour, N. Frosst, G. E. Hinton, in Advances in Neural Information Processing Systems. Dynamic routing between capsules, (2017). http:\/\/arxiv.org\/abs\/1710.09829."},{"issue":"1","key":"197_CR90","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1515\/ijhp-2015-0005","volume":"2","author":"T. Fuhr","year":"2015","unstructured":"T. Fuhr, H. Reetz, C. Wegener, Comparison of supervised-learning models for infant cry classification \/ Vergleich von Klassifikationsmodellen zur S\u00e4uglingsschreianalyse. Int. J. Health Prof. 2(1), 4\u201315 (2015). https:\/\/doi.org\/10.1515\/ijhp-2015-0005.","journal-title":"Int. J. Health Prof"},{"key":"197_CR91","doi-asserted-by":"publisher","unstructured":"R. Sahak, W. Mansor, Y. K. Lee, A. I. M. Yassin, A. Zabidi, in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC\u201910. Performance of combined support vector machine and principal component analysis in recognizing infant cry with asphyxia, (2010), pp. 6292\u20136295. https:\/\/doi.org\/10.1109\/IEMBS.2010.5628084.","DOI":"10.1109\/IEMBS.2010.5628084"},{"key":"197_CR92","doi-asserted-by":"publisher","unstructured":"R. Sahak, W. Mansor, Y. K. Lee, A. I. Mohd Yassin, A. Zabidi, in Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010, 3. Orthogonal least square based support vector machine for the classification of infant cry with asphyxia, (2010), pp. 986\u2013990. https:\/\/doi.org\/10.1109\/BMEI.2010.5639300.","DOI":"10.1109\/BMEI.2010.5639300"},{"key":"197_CR93","doi-asserted-by":"publisher","unstructured":"A. Zabidi, W. Mansor, L. Y. Khuan, I. M. Yassin, R. Sahak, in Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010. The effect of F-ratio in the classification of asphyxiated infant cries using multilayer perceptron neural network, (2010), pp. 126\u2013129. https:\/\/doi.org\/10.1109\/IECBES.2010.5742213.","DOI":"10.1109\/IECBES.2010.5742213"},{"issue":"6","key":"197_CR94","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1093\/ijnp\/pyx014","volume":"20","author":"G. Esposito","year":"2017","unstructured":"G. Esposito, N. Hiroi, M. L. Scattoni, Cry, baby, cry: expression of distress as a biomarker and modulator in autism spectrum disorder. Int. J. Neuropsychopharmacol.20(6), 498\u2013503 (2017). https:\/\/doi.org\/10.1093\/ijnp\/pyx014.","journal-title":"Int. J. Neuropsychopharmacol."},{"key":"197_CR95","doi-asserted-by":"publisher","unstructured":"S. Orlandi, C. Manfredi, L. Bocchi, M. L. Scattoni, in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Automatic newborn cry analysis: a non-invasive tool to help autism early diagnosis, (2012), pp. 2953\u20132956. https:\/\/doi.org\/10.1109\/EMBC.2012.6346583.","DOI":"10.1109\/EMBC.2012.6346583"},{"issue":"2","key":"197_CR96","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1016\/j.cmpb.2011.07.010","volume":"108","author":"M. Hariharan","year":"2012","unstructured":"M. Hariharan, R. Sindhu, S. Yaacob, Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network. Comput. Methods Programs Biomed.108(2), 559\u2013569 (2012). https:\/\/doi.org\/10.1016\/j.cmpb.2011.07.010.","journal-title":"Comput. Methods Programs Biomed."},{"key":"197_CR97","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.bspc.2014.10.002","volume":"17","author":"A. Rosales-P\u00e9rez","year":"2015","unstructured":"A. Rosales-P\u00e9rez, C. A. Reyes-Garc\u00eda, J. A. Gonzalez, O. F. Reyes-Galaviz, H. J. Escalante, S. Orlandi, Classifying infant cry patterns by the Genetic Selection of a Fuzzy Model. Biomed. Signal Process. Control. 17:, 38\u201346 (2015). https:\/\/doi.org\/10.1016\/j.bspc.2014.10.002.","journal-title":"Biomed. Signal Process. Control"},{"key":"197_CR98","doi-asserted-by":"publisher","unstructured":"F. Feier, I. Enatescu, C. Ilie, I. Silea, in 2014 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2014. Newborns\u2019 cry analysis classification using signal processing and data mining, (2014), pp. 880\u2013885. https:\/\/doi.org\/10.1109\/OPTIM.2014.6850990.","DOI":"10.1109\/OPTIM.2014.6850990"},{"key":"197_CR99","doi-asserted-by":"publisher","unstructured":"A. F. Symon, N. Hassan, H. Rashid, I. U. Ahmed, S. M. T. Reza, in 4th International Conference on Advances in Electrical Engineering, ICAEE 2017. Design and development of a smart baby monitoring system based on Raspberry Pi and Pi camera, (2017), pp. 117\u2013122. https:\/\/doi.org\/10.1109\/ICAEE.2017.8255338.","DOI":"10.1109\/ICAEE.2017.8255338"},{"key":"197_CR100","doi-asserted-by":"publisher","unstructured":"V. Hiremath, P. Venkataratnam, in International Conference On Smart Technologies For Smart Nation (SmartTechCon). Automatic cradle system with measurement of baby\u2019s vital biological parameters (Bangalore, 2017), pp. 480\u2013485. https:\/\/doi.org\/10.1109\/SmartTechCon.2017.8358420.","DOI":"10.1109\/SmartTechCon.2017.8358420"},{"key":"197_CR101","doi-asserted-by":"publisher","unstructured":"M. P. Joshi, D. C. Mehetre, in 2017 International Conference on Computing, Communication, Control and Automation, ICCUBEA 2017. IoT based smart cradle system with an Android app for baby monitoring, (2018), pp. 1\u20134. https:\/\/doi.org\/10.1109\/ICCUBEA.2017.8463676.","DOI":"10.1109\/ICCUBEA.2017.8463676"},{"key":"197_CR102","doi-asserted-by":"publisher","first-page":"93791","DOI":"10.1109\/ACCESS.2019.2928481","volume":"7","author":"W. A. Jabbar","year":"2019","unstructured":"W. A. Jabbar, H. K. Shang, S. N. I. S. Hamid, A. A. Almohammedi, R. M. Ramli, M. A. H. Ali, IoT-BBMS: Internet of Things-based baby monitoring system for smart cradle. IEEE Access. 7:, 93791\u201393805 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2928481.","journal-title":"IEEE Access"}],"container-title":["EURASIP Journal on Audio, Speech, and Music Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13636-021-00197-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13636-021-00197-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13636-021-00197-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,5]],"date-time":"2021-02-05T12:32:15Z","timestamp":1612528335000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13636-021-00197-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,5]]},"references-count":102,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["197"],"URL":"https:\/\/doi.org\/10.1186\/s13636-021-00197-5","relation":{},"ISSN":["1687-4722"],"issn-type":[{"value":"1687-4722","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,5]]},"assertion":[{"value":"19 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"8"}}