Infants Cry Classification of Physiological State Using Cepstral and Prosodic Acoustic Features

Authors

  • Ramon L. Rodriguez College of Computer Studies, National University, Philippines
  • Susan S. Caluya College of Computer Studies, National University, Philippines

Keywords:

Audio Features, Audio Signal, Infants Cry, Infants State, Machine Learning Algorithms,

Abstract

Infants cry to express their emotional, psychological and physiological states. The research paper investigates if cepstral and prosodic audio features are enough to classify the infants’ physiological states such as hunger, pain and discomfort. Dataset from our previous paper was used to train the classification algorithm. The results showed that the audio features could classify an infant’s physiological state. We used three classification algorithms, Decision Tree (J48), Neural Network and Support Vector Machine in developing the infant physiological model. To evaluate the performance of the infant physiological state model, Precision, Recall and F-measure were used as performance metrics. Comparison of the cepstral and prosodic audio feature is presented in the paper. Our findings revealed that Decision Tree and Multilayer Perceptron performed better both for cepstral and prosodic feature. It is noted the cepstral feature yielded better result compare with prosodic feature for the given dataset with correctly classified instances ranging from 87.64% to 90.80 with an overall kappa statistic ranging from 0.47 – 0.64 using cepstral feature.

References

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Published

2018-05-31

How to Cite

Rodriguez, R. L., & Caluya, S. S. (2018). Infants Cry Classification of Physiological State Using Cepstral and Prosodic Acoustic Features. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-3), 193–196. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4217

Issue

Vol. 10 No. 2-3: Technology Innovation in Electrical, Electronics and Information Technology

Section

Articles

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