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<p>Mounting evidence points to widespread declines in insect abundance and diversity across European terrestrial ecosystems, highlighting an urgent need for effective large-scale monitoring methods. Passive acoustic monitoring enables the monitoring of sound-producing insects at an unprecedented temporal and spatial scale by remotely capturing sounds such as orthopteran stridulations and cicada timbalizations. However, current automated recognition tools for European insect sounds remain limited, and developing algorithms capable of reliably identifying diverse species requires large, ecologically heterogeneous acoustic datasets. Here we present a dataset of 11,224 recordings covering 193 orthopteran and 24 cicada species from North, Central, and temperate Western Europe. It combines coarsely labeled recordings, for which we can only infer the presence, at some point, of their target species (weak labeling), with finely annotated recordings that specify the time and frequency range of each insect sound (strong labeling). This dataset complements existing online resources and supports the advancement of automated acoustic classification for orthopterans and cicadas, aiding biodiversity monitoring efforts across Europe.</p>

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A finely annotated dataset for the automated acoustic identification of European Orthoptera and Cicadidae

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月7日

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  • David Funosas,
  • Elodie Massol,
  • Yves Bas,
  • Svenja Schmidt,
  • David Bennett,
  • Dominik Arend,
  • Alexander Gebhard,
  • Luc Barbaro,
  • Sebastian König,
  • Rafael Carbonell Font,
  • David Sannier,
  • Fernand Deroussen,
  • Jérôme Sueur,
  • Tomi Trilar,
  • Wolfgang Forstmeier,
  • Lucas Roger,
  • Eloïsa Matheu,
  • Piotr Guzik,
  • Julien Barataud,
  • Laurent Pelozuelo,
  • Stéphane Puissant,
  • Sandra Mueller,
  • Björn Schuller,
  • Jose M. Montoya,
  • Andreas Triantafyllopoulos,
  • Maxime Cauchoix

摘要

Mounting evidence points to widespread declines in insect abundance and diversity across European terrestrial ecosystems, highlighting an urgent need for effective large-scale monitoring methods. Passive acoustic monitoring enables the monitoring of sound-producing insects at an unprecedented temporal and spatial scale by remotely capturing sounds such as orthopteran stridulations and cicada timbalizations. However, current automated recognition tools for European insect sounds remain limited, and developing algorithms capable of reliably identifying diverse species requires large, ecologically heterogeneous acoustic datasets. Here we present a dataset of 11,224 recordings covering 193 orthopteran and 24 cicada species from North, Central, and temperate Western Europe. It combines coarsely labeled recordings, for which we can only infer the presence, at some point, of their target species (weak labeling), with finely annotated recordings that specify the time and frequency range of each insect sound (strong labeling). This dataset complements existing online resources and supports the advancement of automated acoustic classification for orthopterans and cicadas, aiding biodiversity monitoring efforts across Europe.