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The European bee-eater poses a significant challenge to beekeepers due to its predation on honeybees, which are crucial for pollination and honey production. This study presents an innovative approach to enhancing beekeeping practices through the development of a detection system for European bee-eaters using YAMNet and Long Short-Term Memory (LSTM) networks. YAMNet, a deep learning model pre-trained for sound classification, is leveraged to extract acoustic features from environmental audio recordings.

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Acoustic Detection of European Bee-Eaters Using YAMNet and Bi-LSTM Networks for Honeybee Protection

  • 会议论文

  • 发表日期：

    2025年

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  • Sonia Mosbah,
  • Dorsaf Hrizi,
  • Ismail Bokri,
  • Eya Ben Moulehem,
  • Mehdi Fgaier,
  • Nour Ben Ammar,
  • Malek Gharsallah,
  • Nadia Trabelsi

摘要

The European bee-eater poses a significant challenge to beekeepers due to its predation on honeybees, which are crucial for pollination and honey production. This study presents an innovative approach to enhancing beekeeping practices through the development of a detection system for European bee-eaters using YAMNet and Long Short-Term Memory (LSTM) networks. YAMNet, a deep learning model pre-trained for sound classification, is leveraged to extract acoustic features from environmental audio recordings.