Abstract
Crying is the infant’s first verbal communication. Before learning how to express the emotions or physiological/psychological requirements with language, infants usually express how they feel through crying. Crying is a response to a stimulus such as hunger, pain, or discomfort. However, it is sometimes difficult to figure out why an infant is crying. This can certainly be frustrating or even frightening for a caretaker, and so we in this paper have proposed an infant cry classification system to categorize the types of infant crying to help parents and nursing staffs attending to the needs of the infants. Currently, three kinds of distinct infant cries have been identified: hunger; pain; and feeling sleepy. Fifteen features are extracted from each crying frame and the sequential forward floating selection is then adopted to pick out high discriminative features. The directed acyclic graph support vector machine is finally used to classify infant crying. Experimental results have revealed the good performance of the proposed system and the classification accuracy is up to 92.17 %.
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This work was supported by Ministry of Science and Technology, Taiwan, under the Grants NSC 100-2218-E-224-007-MY3.
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Chang, CY., Chang, CW., Kathiravan, S. et al. DAG-SVM based infant cry classification system using sequential forward floating feature selection. Multidim Syst Sign Process 28, 961–976 (2017). https://doi.org/10.1007/s11045-016-0404-5
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DOI: https://doi.org/10.1007/s11045-016-0404-5