Deep Learning for Infant Cry Recognition

Yun-Chia Liang, Iven Wijaya, Ming-Tao Yang, Josue Rodolfo Cuevas Juarez and Hou-Tai Chang
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Yun-Chia Liang: Department of Industrial Engineering and Management, Yuan Ze University, No. 135, Yuan-Tung Rd., Chung-Li Dist., Taoyuan City 32003, Taiwan
Iven Wijaya: Department of Industrial Engineering and Management, Yuan Ze University, No. 135, Yuan-Tung Rd., Chung-Li Dist., Taoyuan City 32003, Taiwan
Ming-Tao Yang: Department of Chemical Engineering and Materials Science, Yuan Ze University, No. 135, Yuan-Tung Rd., Chung-Li Dist., Taoyuan City 32003, Taiwan
Josue Rodolfo Cuevas Juarez: Department of Industrial Engineering and Management, Yuan Ze University, No. 135, Yuan-Tung Rd., Chung-Li Dist., Taoyuan City 32003, Taiwan
Hou-Tai Chang: Department of Industrial Engineering and Management, Yuan Ze University, No. 135, Yuan-Tung Rd., Chung-Li Dist., Taoyuan City 32003, Taiwan

IJERPH, 2022, vol. 19, issue 10, 1-10

Abstract: Recognizing why an infant cries is challenging as babies cannot communicate verbally with others to express their wishes or needs. This leads to difficulties for parents in identifying the needs and the health of their infants. This study used deep learning (DL) algorithms such as the convolutional neural network (CNN) and long short-term memory (LSTM) to recognize infants’ necessities such as hunger/thirst, need for a diaper change, emotional needs (e.g., need for touch/holding), and pain caused by medical treatment (e.g., injection). The classical artificial neural network (ANN) was also used for comparison. The inputs of ANN, CNN, and LSTM were the features extracted from 1607 10 s audio recordings of infants using mel-frequency cepstral coefficients (MFCC). Results showed that CNN and LSTM both provided decent performance, around 95% in accuracy, precision, and recall, in differentiating healthy and sick infants. For recognizing infants’ specific needs, CNN reached up to 60% accuracy, outperforming LSTM and ANN in almost all measures. These results could be applied as indicators for future applications to help parents understand their infant’s condition and needs.

Keywords: infant cry recognition; convolutional neuron network; long short-term memory; deep learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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