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Automatic Baby Cry Detector with sleep music player (ABCD)

Kanha Malviya, Navneet Kaur, Aniket Brahamne, Nikhil Kumar, Mohit Suryawanshi

Abstract


In today’s world, our lives have become more
dependent on technology. One problem which caught our eyes
is when parents have to leave their wards (aged between 3
months to 2 years) alone due to some essential tasks for a short
duration, the baby goes unmonitored. Normally, parents do
this by leaving their child asleep. In many cases, the child
wakes up and starts to cry. In absence of loved ones, babies
need immediate calmness relief. A baby’s cry can be
characterized by its natural periodic tone and the change of its
voice. This study of sound recognition involves feature
extraction and classification by determining the sound pattern.
The solution to this problem is, Automatic Baby Cry Detector
with sleep music player (ABCD), which is programmed on
Python and the heart of this device is an Audio learning model.
Of this model, a Deep Learning algorithm works by first
extracting MFCCs of the filtered audio. Then, these sound
features (extracted by MFCCs) are predicted by the trained
Model. This trained model identifies the Baby Cry by
classifying audio using K-Nearest Neighbor (k-NN) method.
Based on the probability confirmer (for model accuracy), this
model confirms a baby’s cry. Then the program can further
play a lullaby (programmable) and also alert the parents by
sending a notification on their phone.


Keywords


Feature Extraction, Classification by Sound Pattern, Automatic Baby Cry Detector (ABCD), Python, Audio Learning Model, Mel-Frequency Cepstral Coefficient (MFCC), K-Nearest Neighbor (k-NN), Probability Confirmer, Lullaby, Notification.

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DOI: https://doi.org/10.37591/joma.v10i1.7009

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