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: convolutional neuron network; deep learning; infant cry recognition; long short-term memory.