公开459种昆虫声音数据集，助力机器学习识别生物多样性变化
InsectSet459: an open dataset of insect sounds for bioacoustic machine learning
- 收集459种直翅目和蝉类昆虫的26399段音频，覆盖广泛频率范围
- 在两种先进深度学习模型上测试，分类性能良好但仍有提升空间
- 适合开发处理高频变率音频的新型声学表征方法，适合生态监测研究
自动识别昆虫声音有助于理解全球生物多样性变化趋势——但昆虫声音对深度学习而言仍具挑战。本文提出首个大规模昆虫声音开放数据集InsectSet459，包含459种直翅目与蝉类昆虫的26399段音频，采用多种录音设备与采样率录制，覆盖昆虫发声的极宽频率范围。我们以两种最先进的深度学习分类器进行基准测试，结果显示分类性能良好，但仍存在显著提升空间。该数据集可作为构建昆虫监测工作流的真实测试案例，也是开发能应对高度变异性频率与采样率的音频表示方法的挑战性基础。
原文摘要 · Abstract (English)
Automatic recognition of insect sound could help us understand changing biodiversity trends around the world -- but insect sounds are challenging to recognize even for deep learning. We present a new dataset comprised of 26399 audio files, from 459 species of Orthoptera and Cicadidae. It is the first large-scale dataset of insect sound that is easily applicable for developing novel deep-learning methods. Its recordings were made with a variety of audio recorders using varying sample rates to capture the extremely broad range of frequencies that insects produce. We benchmark performance with two state-of-the-art deep learning classifiers, demonstrating good performance but also significant room for improvement in acoustic insect classification. This dataset can serve as a realistic test case for implementing insect monitoring workflows, and as a challenging basis for the development of audio representation methods that can handle highly variable frequencies and/or sample rates.
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