Baby cry recognition using deep neural networks

Abstract

Infant cry recognition is a challenging task as it is hard to determine the speech features that can allow researchers to clearly separate between different types of cries. However, baby cry is treated as a different way of communication of speech. The types of baby cry can be differentiated using Mel-Frequency Cepstral Coefficient (MFCC) with appropriate artificial intelligence model. Stacked restricted Boltzmann machine (RBN) is popular in providing few layers of neural networks to convert the high dimensional data to lower dimensional data to fine tune the input data to a better initialized weight for the neural networks. Usually RBN is used with another deep neural network to form the deep belief networks (DBN), and the studies in this direction is heading towards the convolutional-RBN variant. The study on RBN to pre-train Convolutional neural networks (CNN) without convolution function in the RBN meanwhile is scarce due to the Back propagation and principal component analysis can be applied directly to the CNN. In this paper, we describe the hybrid system between RBN and CNN for learning class specific features for baby cry recognition using the feature of Mel-Frequency Cepstral Coefficient. We archived an 78.6% of accuracy on 5 types of baby cries by validating the proposed model on baby cry recognition.