在低功耗设备上实现多物种鸟类音频实时识别，助力生态监测
Enabling Multi-Species Bird Classification on Low-Power Bioacoustic Loggers
- 设计半可学习频谱特征提取器，适应鸟类鸣叫特点
- 在70种鸟数据集上准确率达70.1%，部分物种超90.8%
- 每推理仅耗电77mJ，比Birdnet节能16倍以上
本文提出WrenNet，一种高效神经网络，可在低功耗微控制器上实现实时多物种鸟类音频分类，支持大规模生物多样性监测。我们设计了一种半可学习的频谱特征提取器，能自适应鸟类鸣叫特性，优于标准梅尔尺度和全可学习方法。在人工精选的70种鸟类数据集上，WrenNet对声学特征明显的物种识别准确率达90.8%，整体任务准确率为70.1%。部署于AudioMoth设备（≤1MB RAM）时，每次推理仅消耗77mJ能量。相比在Raspberry Pi 3B+上运行的Birdnet，该模型能效提升超过16倍。本研究首次实现了低功耗边缘设备上的连续多物种声学监测框架。
原文摘要 · Abstract (English)
This paper introduces WrenNet, an efficient neural network enabling real-time multi-species bird audio classification on low-power microcontrollers for scalable biodiversity monitoring. We propose a semi-learnable spectral feature extractor that adapts to avian vocalizations, outperforming standard mel-scale and fully-learnable alternatives. On an expert-curated 70-species dataset, WrenNet achieves up to 90.8\% accuracy on acoustically distinctive species and 70.1\% on the full task. When deployed on an AudioMoth device ($\leq$1MB RAM), it consumes only 77mJ per inference. Moreover, the proposed model is over 16x more energy-efficient compared to Birdnet when running on a Raspberry Pi 3B+. This work demonstrates the first practical framework for continuous, multi-species acoustic monitoring on low-power edge devices.
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