Naturalis Biodiversity Center, Leiden, The Netherlands.
Leiden University, Leiden, The Netherlands.
PLoS Comput Biol. 2023 Oct 4;19(10):e1011541. doi: 10.1371/journal.pcbi.1011541. eCollection 2023 Oct.
Insect population numbers and biodiversity have been rapidly declining with time, and monitoring these trends has become increasingly important for conservation measures to be effectively implemented. But monitoring methods are often invasive, time and resource intense, and prone to various biases. Many insect species produce characteristic sounds that can easily be detected and recorded without large cost or effort. Using deep learning methods, insect sounds from field recordings could be automatically detected and classified to monitor biodiversity and species distribution ranges. We implement this using recently published datasets of insect sounds (up to 66 species of Orthoptera and Cicadidae) and machine learning methods and evaluate their potential for acoustic insect monitoring. We compare the performance of the conventional spectrogram-based audio representation against LEAF, a new adaptive and waveform-based frontend. LEAF achieved better classification performance than the mel-spectrogram frontend by adapting its feature extraction parameters during training. This result is encouraging for future implementations of deep learning technology for automatic insect sound recognition, especially as larger datasets become available.
昆虫种群数量和生物多样性随时间迅速减少，监测这些趋势对于有效实施保护措施变得越来越重要。但是，监测方法通常具有侵入性，耗费时间和资源，并且容易受到各种偏差的影响。许多昆虫物种会产生特征性的声音，这些声音可以很容易地被检测和记录下来，而不需要大量的成本或努力。使用深度学习方法，可以自动检测和分类昆虫的声音，从而监测生物多样性和物种分布范围。我们使用最近发布的昆虫声音数据集（多达 66 种直翅目和蝉科）和机器学习方法来实现这一点，并评估它们在声学昆虫监测中的潜力。我们比较了基于传统声谱图的音频表示与 LEAF（一种新的自适应和基于波形的前端）的性能。在训练过程中，LEAF 通过自适应其特征提取参数，实现了比梅尔频谱前端更好的分类性能。这一结果对于未来自动昆虫声音识别的深度学习技术的实现是令人鼓舞的，特别是随着更大的数据集的出现。
