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An Infant Cry Recognition based on Convolutional Neural Network Method
Author(s)
Teeravajanadet, K.
Siwilai, N.
Thanaselanggul, K.
Ponsiricharoenphan, N.
Date Issued
November 1, 2019
Type
Conference Paper
Abstract
In this paper, an investigation of crying signal spectra is used to classify categories of infant cries. Three different types of crying considered in this work are hungry, sleepy and burping need. These cries are preprocessed and converted for calculation of Mel-Frequency Cepstral Coefficients (MFCC) before being classified by Convolutional Neural Network (CNN). Experimental results show that CNN based deep learning achieves high performance of 84%.
Citation
Bmeicon 2019 12th Biomedical Engineering International Conference, 2019