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 (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.

翻译：昆虫种群数量与生物多样性正随时间快速下降，监测这些趋势对于有效实施保护措施日益重要。但现有监测方法常具侵入性、耗费大量时间与资源，且易受多种偏差影响。许多昆虫物种能发出特征性声音，这些声音无需巨大成本或努力即可轻松检测和记录。利用深度学习方法，可从野外录音中自动检测并分类昆虫声音，以监测生物多样性与物种分布范围。我们采用近期发布的昆虫声音数据集（直翅目和蝉科）及机器学习方法实施该方案，并评估其用于声学昆虫监测的潜力。我们比较了传统基于语谱图的音频表征与LEAF（一种新型自适应波形前端）的性能。LEAF通过在训练过程中自适应调整特征提取参数，其分类性能优于梅尔谱图前端。这一结果令人鼓舞，预示着深度学习技术在未来自动昆虫声音识别中的应用前景，尤其在更大规模数据集可用之时。