Autonomous Surveillance of Infants’ Needs Using CNN Model for Audio Cry Classification ()
ABSTRACT
Infants portray suggestive unique cries while sick, having belly pain, discomfort, tiredness, attention
and desire for a change of diapers among other needs. There exists limited knowledge in accessing
the infants’ needs as they only relay
information through suggestive cries. Many teenagers tend to give birth at an early
age, thereby exposing them to be the key monitors of their own babies. They
tend not to have sufficient skills in monitoring the infant’s dire needs, more so during the early stages of infant development.
Artificial intelligence has shown promising efficient predictive analytics
from supervised, and unsupervised to reinforcement learning models. This study, therefore, seeks to develop an android app that could be used to discriminate
the infant audio cries by leveraging the strength of convolution neural networks
as a classifier model. Audio analytics from many kinds of literature is an untapped area
by researchers as it’s attributed to messy and
huge data generation. This study, therefore, strongly leverages convolution neural networks, a deep learning
model that is capable of handling more
than one-dimensional datasets. To achieve
this, the audio data in form of a wave was converted to images through Mel spectrum frequencies
which were classified using the computer vision CNN model. The Librosa
library was used to convert the audio to Mel spectrum which was then presented as pixels serving as the input for classifying
the audio classes such as sick, burping, tired, and hungry. The study goal was to
incorporate the model as an android tool that can be utilized at the domestic level and hospital
facilities for surveillance of the infant’s health
and social needs status all time round.
Share and Cite:
Owino, G. , Waititu, A. , Wanjoya, A. and Okwiri, J. (2022) Autonomous Surveillance of Infants’ Needs Using CNN Model for Audio Cry Classification.
Journal of Data Analysis and Information Processing,
10, 198-219. doi:
10.4236/jdaip.2022.104013.
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