Open AccessBaby Cry Detection in Domestic Environment using Convolutional Neural Networks
Author(s) -
L. Arokia Jesu Prabhu,
M M. Reethu,
M. Sabaritha,
S. Santhiya,
N. Subramanian
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.g5260.059720
Subject(s) - artificial neural network , computer science , convolutional neural network , artificial intelligence , speech recognition , psychology
In this paper we will identify a cry signals of infants and the explanation behind the screams below 0-6 months of segment age. Detection of baby cry signals is essential for the pre-processing of various applications involving crial analysis for baby caregivers, such as emotion detection. Since cry signals hold baby well-being information and can be understood to an extent by experienced parents and experts. We train and validate the neural network architecture for baby cry detection and also test the fastAI with the neural network. Trained neural networks will provide a model and this model can predict the reason behind the cry sound. Only the cry sounds are recognized, and alert the user automatically. Created a web application by responding and detecting different emotions including hunger, tired, discomfort, bellypain.