Convolutional Neural Networks for Audio-Based Continuous Infant Cry Monitoring at Home
Jiali Xie, Xi Long, R.A. Otte, Caifeng Shan
Abstract
Cry is an important signal in early infancy for parents to understand needs of their baby and thereby to provide timely parenting/soothing or to be reassured. Thanks to the recent advancement of signal processing, deep learning, and internet-of-things technologies, smart baby monitors with a microphone and/or a video camera have attracted a lot of attention to be used in a baby room to assist parental activities. In this paper, we propose a two-step approach to detect infant cries automatically with continuous audio signals. We first identify and remove the segments without clear sounds (background noise) using a volume-based thresholding algorithm, followed by convolutional neural network (CNN) models to further detect infant cries. The CNN operates on the log linear-scale filterbank energies of audio signals to extract features for cry detection. In this study, a large set of audio data (151.8 hours) collected from five infants in home settings were included. Our proposed approach achieved a mean accuracy of 98.6% in identifying background noise (with only 2 out of 3209 cry segments missed) and a mean accuracy of 92.2% in detecting cries from other non-background sounds.