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<p>The beehive sound, a continuous signal produced by bees within the hive, has been found to correlate with different behavioral states of the colony, like being queenless and swarming. We investigated the possibility of identifying foraging-related cues in this signal. We recorded a colony’s sound while foraging from food sources located at three different distances from the hive, one at a time. The recordings were split into frames to obtain six statistics of their Mel Frequency Cepstral Coefficients. Then, we evaluated different autoencoding networks to obtain a latent space that allowed frames from different foraging sources to be easily differentiable. The high Accuracy, Silhouette score, and F1-score shown in the obtained latent spaces strongly support our approach for identifying foraging-related cues in beehive sound activity.</p>

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Identification of foraging-related cues in beehive sound activity using machine learning methods

  • 期刊论文

  • 发表日期：

    2025年11月18日

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  • David Bocanegra,
  • Jorge Galvez,
  • Nayan Di,
  • Fanglin Liu,
  • Fernando Wario

摘要

The beehive sound, a continuous signal produced by bees within the hive, has been found to correlate with different behavioral states of the colony, like being queenless and swarming. We investigated the possibility of identifying foraging-related cues in this signal. We recorded a colony’s sound while foraging from food sources located at three different distances from the hive, one at a time. The recordings were split into frames to obtain six statistics of their Mel Frequency Cepstral Coefficients. Then, we evaluated different autoencoding networks to obtain a latent space that allowed frames from different foraging sources to be easily differentiable. The high Accuracy, Silhouette score, and F1-score shown in the obtained latent spaces strongly support our approach for identifying foraging-related cues in beehive sound activity.