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In biodiversity research scientists now routinely acquire audio recordings of vocalizing bird species and are then faced with the task of identifying the species audible in these recordings. Here, we analyze the accuracy (precision, recall and \(F_1\) score) of several deep networks, in conjunction with pre-training and data augmentation techniques, for classifying audio recordings of twelve bird species under multiple data scarcity settings.

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Fine-Tuning for Bird Sound Classification: An Empirical Study

  • 会议论文

  • 发表日期：

    2025年

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  • David Stein,
  • Bjoern Andres

摘要

In biodiversity research scientists now routinely acquire audio recordings of vocalizing bird species and are then faced with the task of identifying the species audible in these recordings. Here, we analyze the accuracy (precision, recall and \(F_1\) score) of several deep networks, in conjunction with pre-training and data augmentation techniques, for classifying audio recordings of twelve bird species under multiple data scarcity settings.