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Analyzing the Impact of Plot Size in Vision-Based Time-Series Sound Classification

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(11), pp.49-56
  • DOI : 10.9708/jksci.2024.29.11.049
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 4, 2024
  • Accepted : November 5, 2024
  • Published : November 29, 2024

Euihyun Jung 1

1안양대학교

Accredited

ABSTRACT

In recent years, visualizing time-series data as images for use in vision-based Artificial Intelligence (AI) models has gained significant attention. This approach transforms temporal sequences into images that can be processed by deep learning models, such as Convolutional Neural Network (CNN). Although its effectiveness has been demonstrated in various domains, the impact of plot size on model performance remains underexplored. In this study, we investigate the effect of varying plot sizes on classification accuracy by visualizing natural sounds (e.g., cats, crows) and testing five classes of 2,000 samples each using the YOLO model. While training was conducted on 320x320 plots, test sets were generated at six sizes (112x112 to 640x640). Results show that as the plot size of the test dataset diverged from that of the training dataset, both precision and recall decreased, highlighting the importance of plot size consistency in time-series visualization research.

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