Baby Cry Sound Detection: A Comparison of Mel Spectrogram Image on Convolutional Neural Network Models

  • Ridha Fahmi Junaidi Lambung Mangkurat University
  • Mohammad Reza Faisal Lambung Mangkurat University
  • Andi Farmadi Lambung Mangkurat University
  • Rudy Herteno Lambung Mangkurat University
  • Dodon Turianto Nugrahadi Lambung Mangkurat University
  • Luu Duc Ngo Bac Lieu University
  • Bahriddin Abapihi Halu Oleo University
Keywords: TERMS baby cry sound detection, Convolutional Neural Network, Mel Spectrogram, audio classification

Abstract

Baby cries contain patterns that indicate their needs, such as pain, hunger, discomfort, colic, or fatigue. This study explores the use of Convolutional Neural Network (CNN) architectures for classifying baby cries using Mel Spectrogram images. The primary objective of this research is to compare the effectiveness of various CNN architectures such as VGG-16, VGG-19, LeNet-5, AlexNet, ResNet-50, and ResNet-152 in detecting baby needs based on their cries. The datasets used include the Donate-a-Cry Corpus and Dunstan Baby Language. The results show that AlexNet achieved the best performance with an accuracy of 84.78% on the Donate-a-Cry Corpus dataset and 72.73% on the Dunstan Baby Language dataset. Other models like ResNet-50 and LeNet-5 also demonstrated good performance although their computational efficiency varied, while VGG-16 and VGG-19 exhibited lower performance. This research provides significant contributions to the understanding and application of CNN models for baby cry classification. Practical implications include the development of baby cry detection applications that can assist parents and healthcare provide.

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Published
2024-09-16
How to Cite
[1]
R. F. Junaidi, “Baby Cry Sound Detection: A Comparison of Mel Spectrogram Image on Convolutional Neural Network Models”, j.electron.electromedical.eng.med.inform, vol. 6, no. 4, pp. 355-369, Sep. 2024.
Section
Electronics