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This paper introduces DrongoNet, a family of ultra-compact CNN architectures specifically designed for detecting tropical bird vocalizations using low-power microcontrollers. By addressing the limitations of traditional coarse frequency-energy triggers and existing neural detectors, DrongoNet-Micro achieves a remarkable 0.9810 AUC with only 919 parameters, significantly improving the precision of bird detection while extending recording durations. The models are validated on a comprehensive dataset and demonstrate deployment flexibility across various environments, making them suitable for enhancing biodiversity monitoring efforts in tropical regions.
DrongoNet-Micro captures 8 percentage points more bird vocalizations than traditional methods while using 28 times fewer parameters, revolutionizing acoustic monitoring in biodiversity-rich environments.
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 {\tau} = 0.35), is a drop-in replacement for the Goertzel trigger used in commodity field recorders: at {\alpha} = 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.