Abstract
Crying is the only communication way recently born babies have to express their needs. Several studies have shown that infant cry can be a valuable tool to determine the different infant’s emotional, and physiological states. With the aim in usefully applying the crying information, in this paper we present the use of Fuzzy Support Vector Machines (FSVM) for two different infant cry recognition tasks. In the first one to identify pathologies, we classify Normal, Deaf, and Asphyxia infant cries. The second problem is about identifying Pain cries, Hunger cries and No-Pain-No-Hunger cries which are those that do not belong to any of the first two classes. Here we show that FSVM perform better than conventional SVM reaching a correct classification accuracy of up to 90%.
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© 2006 Springer-Verlag Berlin Heidelberg
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Barajas-Montiel, S.E., Reyes-García, C.A. (2006). Fuzzy Support Vector Machines for Automatic Infant Cry Recognition. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing in Signal Processing and Pattern Recognition. Lecture Notes in Control and Information Sciences, vol 345. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37258-5_107
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DOI: https://doi.org/10.1007/978-3-540-37258-5_107
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