CLASSIFICATION OF BABY CRIES INTO DISTINCT CATEGORIES USING CONVOLUTIONAL NEURAL NETWORKS(CNN) WITH SOUND AND SPECTROGRAM ANALYSIS
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Date
2024-03
Authors
Tulegenov, Maxat
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
The act of a baby crying is a complex form of communication that reflects various
physical, medical, and emotional states. Understanding the nuances within baby
cries is essential, as it provides valuable insights into the baby’s needs and can
assist in the early detection of developmental disorders and medical conditions.
Machine Learning (ML) and Deep Learning (DL) techniques, specifically
Convolutional Neural Networks (CNNs), coupled with sound processing and data
augmentation, play a pivotal role in this endeavor. This research explores
methods encompassing data preprocessing, feature extraction, postprocessing,
and classification. A primary focus is acoustic analysis and CNN for automatic
feature extraction.
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Type of access: Restricted
Citation
Tulegenov, M. (2024). Classification of Baby Cries into Distinct Categories using Convolutional Neural Networks(CNN) with Sound and Spectrogram Analysis. Nazarbayev University School of Engineering and Digital Sciences