用于微控制器上热带鸟类音频检测的超紧凑型卷积神经网络架构
Ultra-Compact CNN Architectures for Tropical Bird Audio Detection on Microcontrollers
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- Faculty of Computer Science and Information Technology, Universiti Malaya（马来西亚大学计算机科学与信息技术学院）
- Faculty of Electrical Engineering, Universiti Teknologi Malaysia（马来西亚理工大学电气工程学院）
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中文总结 AI 辅助
研究针对热带鸟类音频检测，提出DrongoNet系列超紧凑型卷积神经网络架构。通过在低功耗微控制器上仅在可能阳性片段触发记录，降低存储和电池成本。该架构在SEABAD数据集验证，不同型号各有优势，且全INT8量化成本低，能在不同环境部署。
中文摘要 AI 辅助
热带生物多样性的被动声学监测因持续记录声景的存储和电池成本而受限，鸟类叫声在音频中占比不到10%。基于低功耗微控制器的自主记录单元仅在可能为阳性的片段触发来解决此问题，但现有设备选项不尽人意。我们提出了DrongoNet，这是一组针对此环境设计的三个INT8卷积神经网络探测器，并在一个包含50000个音频片段、1677种东南亚热带数据集（SEABAD）上进行了验证。DrongoNet-Micro可替代商品现场记录仪中使用的Goertzel触发器，在热带流行率为0.10时，比Goertzel多捕获8个百分点的鸟类叫声，并将32GB卡的监测时间从约28天延长到约45天。DrongoNet-Nano限制超低闪存极限，DrongoNet-Edge针对Linux SBC。在SEABAD上，Micro在参数减少28倍的情况下，AUC与重新训练的TinyChirp CNN-Mel基线相差不超过0.1个百分点。所有三个变体的全INT8量化成本降低不到0.12%的AUC。
英文摘要
Passive acoustic monitoring of tropical biodiversity is bottlenecked by the storage and battery cost of continuously recording soundscapes in which bird vocalisations typically occupy less than 10% of the audio. Autonomous recording units built on low-power microcontrollers (typically ARM Cortex-M with $\leq$256 kB of RAM) address this by triggering only on likely-positive segments, but the on-device options are unsatisfying: coarse frequency-energy triggers such as Goertzel filters flood SD cards with false positives at $\sim$71% precision, whereas neural detectors developed for temperate single-species tasks are either too large to deploy or transfer poorly to species-rich tropical settings. We present DrongoNet, a family of three INT8 CNN detectors sized for this envelope and validated on a 50,000-clip, 1,677-species Southeast Asian tropical dataset (SEABAD). The headline model, DrongoNet-Micro (919 parameters, 6.26 kB, 0.9810 AUC, 98.3\% mean recall at τ = 0.35), is a drop-in replacement for the Goertzel trigger used in commodity field recorders: at α = 0.10 tropical prevalence it captures 8 pp more bird vocalisations than Goertzel and extends a 32 GB card from $\sim$28 to $\sim$45 days of monitoring. DrongoNet-Nano (5.09 kB) bounds the ultra-low-flash extreme; DrongoNet-Edge (33.06 kB, 0.9991 AUC) targets Linux SBCs. On SEABAD, Micro matches a retrained TinyChirp CNN-Mel baseline within 0.1 pp AUC at 28$\times$ fewer parameters, confirming that the family is deployment-agnostic across mel-spectrogram bird corpora but requires per-environment retraining. Full INT8 quantisation costs $<$0.12% AUC across all three variants.
