A review: survey on automatic infant cry analysis and classification

Author:

Jeyaraman Saraswathy,Muthusamy Hariharan,Khairunizam Wan,Jeyaraman Sarojini,Nadarajaw Thiyagar,Yaacob Sazali,Nisha Shafriza

Publisher

Springer Science and Business Media LLC

Subject

Biomedical Engineering,Applied Microbiology and Biotechnology,Bioengineering,Biotechnology

Reference100 articles.

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.

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.

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