Baby cry sound detection: A comparison of hand crafted features and deep learning approach

Baby cry sound detection allows parents to be automatically alerted when their baby is crying. Current solutions in home environment ask for a client-server architecture where an end-node device streams the audio to a centralized server in charge of the detection. Even providing the best performances, these solutions raise power consumption and privacy issues. For these reasons, interest has recently grown in the community for methods which can run locally on battery-powered devices. This work presents a new set of features tailored to baby cry sound recognition, called hand crafted baby cry (HCBC) features. The proposed method is compared with a baseline using mel-frequency cepstrum coefficients (MFCCs) and a state-of-the-art convolutional neural network (CNN) system. HCBC features result to be on par with CNN, while requiring less computation effort and memory space at the cost of being application specific.