Monitoring infant's emotional cry in domestic environments using the capsule network architecture

Abstract

Automated recognition of an infant's cry from audio can be considered as a preliminary step for the applications like remote baby monitoring. In this paper, we implemented a recently introduced deep learning topology called capsule network (CapsNet) for the cry recognition problem. A capsule in the CapsNet, which is defined as a new representation, is a group of neurons whose activity vector represents the probability that the entity exists. Active capsules at one level make predictions, via transformation matrices, for the parameters of higher-level capsules. When multiple predictions agree, a higher level capsule becomes active. We employed spectrogram representations from the short segments of an audio signal as an input of the CapsNet. For experimental evaluations, we apply the proposed method on INTERSPEECH 2018 computational paralinguistics challenge (ComParE), crying sub-challenge, which is a three-class classification task using an annotated database (CRIED). Provided audio samples contains recordings from 20 healthy infants and categorized into the three classes namely neutral, fussing and crying. We show that the multi-layer CapsNet is competitive with the baseline performance on the CRIED corpus and is considerably better than a conventional convolutional net.