Abstract
The early identification of forest wood-boring pests is essential for effective pest management. However, detecting infestation in the early stages is difficult, as larvae, such as the emerald ash borer (EAB), Agrilus planipennis Fairmaire (Coleoptera: Buprestidae), usually feed inside the trees. Acoustic sensors can detect the pulse signals generated by larval feeding or movement, but these sounds are often weak and easily masked by background noise. To address this, we propose a dual-branch time-frequency multi-dilated dense network (DBMDNet) for noise reduction. Our model decouples two denoising training objectives: a magnitude masking decoder for coarse denoising and a complex spectral decoder for further magnitude repair and phase correction. Additionally, to enhance global time-frequency modeling, we use three different multi-dilated dense blocks to effectively separate clean signals from noisy data. Given the difficult acquisition of clean larval activity signals, we describe a self-supervised training procedure that utilizes only noisy larval activity signals directly collected from the wild, without the need for paired clean signals. Experimental results demonstrate that our proposed approach achieves the optimal performance on various evaluation metrics while requiring fewer parameters (only 98.62 k) compared to competitive models, achieving an average signal-to-noise ratio (SNR) improvement of 17.45 dB and a log-likelihood ratio (LLR) of 0.14. Furthermore, using the larval activity signals enhanced by DBMDNet, most of the noise is suppressed, and the accuracy of the recognition model is also significantly improved.
Published Version
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