Lean TinyML CNNs for binary bird activity detection on embedded hardware. Derived from the TinyChirp CNN-Mel architecture and trained on the SEABAD (South-East Asian Bird Activity Detection) dataset. Named for the drongo, a vocally versatile Old World tropical passerine — the architecture itself is dataset-independent (see Citation).
| Variant | Hardware | Size | Recall | AUC (INT8, 3 seeds) |
|---|---|---|---|---|
| DrongoNet-Nano | ARM Cortex-M4 (AudioMoth, STM32F4) | 5.09 KB INT8 | — | 0.9727 ± 0.0007 |
| DrongoNet-Micro | ARM Cortex-M4 (AudioMoth, STM32F4) | 6.23 KB INT8 | ≥0.987 @ τ=0.30 | 0.9803 ± 0.0012 |
| DrongoNet-Edge | SBC (Raspberry Pi, Portenta X8) | 33.06 KB INT8 | ≥0.99 @ τ=0.50 | 0.9990 ± 0.0002 |
Recall is the primary deployment metric. AUC is reported for comparison.
pre-ablation/ Phase 0 TinyChirp baselines + zero-shot SEABAD evaluations
develop/ ablation chain (Phase 1–6) and final training scripts
analysis/ threshold sweeps, table compilation, figure generation
benchmark/ cross-dataset benchmarks (DCASE-2018 BAD, TinyChirp Corn Bunting)
deploy/ pre-trained INT8 TFLite models + firmware conversion (deploy/convert_xxd.sh)
edge_deploy/ Raspberry Pi inference package for DrongoNet-Edge
SEABAD — binary classification (bird active / absent), 16 kHz, 3-second clips, 80/10/10 split. Available at zenodo.org/records/18290494.
Mel caches are keyed by (n_mels, n_fft, hop_length) and stored on an external drive:
/Volumes/Evo/cache4arxiv_fft{n_fft}_m{n_mels}/
Pre-trained INT8 TFLite models (seed 42) are in deploy/ — use them directly with deploy/convert_xxd.sh to embed in firmware.
To retrain from scratch, two arguments are required — everything else is locked:
python deploy/train_micro.py \
--dataset-path /path/to/seabad \
--cache-dir /path/to/cache_fft1024_m16Results land in results/drongonet_micro_fft1024_m16_s42/ and include float32 + INT8 TFLite evaluation, confusion matrix, ROC/PR curves, and a parseable results_summary.txt.
- Python 3.10+, TensorFlow 2.15
- librosa, numpy, scikit-learn, matplotlib
M. Zabidi, "DrongoNet: Lightweight CNNs for Tropical Bird Audio Detection on Edge Devices," manuscript in preparation, 2026.
Based on: Huang et al., "TinyChirp: Bird Song Recognition Using TinyML," 2024.