Asymmetric Responsive Guardian for Uninterrupted Species Monitoring
ARGUS is an uninterrupted, two-tier acoustic biologger designed for long-term wildlife monitoring in remote environments.
Inspired by Argus Panoptes --- the guardian who never slept --- ARGUS listens continuously at low power and activates high-intelligence processing only when a biological event occurs.
Goal: >98% recall of bird activity while enabling months of deployment on a single battery.
Field monitoring systems face a difficult trade-off:
- Continuous listening drains batteries
- Aggressive power saving risks missing rare events
- Storing everything creates massive data volume
ARGUS solves this with asymmetric intelligence:
- A lightweight Sentinel listens continuously
- A powerful Classifier wakes only when needed
No missed birds. No wasted power.
ARGUS runs on a dual-core microcontroller (Cortex-M4 + Cortex-M7).
- Always listening at ultra-low power
- Lightweight bird activity detection
- 3-second wake cycles via RTC
- Activates Tier 2 only when bird presence is detected
Priority: Maximize recall.
- Wakes only upon gatekeeper trigger
- Performs species-level classification
- Logs metadata or stores audio
- Returns system to low-power state
Priority: Accuracy and efficiency.
- 50,000 labeled 3-second clips (16 kHz)
- Binary: Bird / No Bird
- Optimized to prevent false negatives
https://github.com/mun3im/seabad
- Custom 4-layer CNN (<8 KB INT8)
- Input: 16×184 mel spectrogram (
n_fft=512) - <8 ms inference on Cortex-M4
- ≥99% recall target
- 7,200 labeled clips
- 12 Southeast Asian species
- Ornithologist-verified
https://github.com/mun3im/mygardenbird
- Depthwise Separable CNN + SE + Attention
- Input: 64×300 mel spectrogram (
n_fft=1024) - 92% top-1 accuracy
- <45 ms inference on Cortex-M7
- Unknown species → audio saved for review
https://github.com/mun3im/mynanet
- Platform: Arduino Portenta H7
- FreeRTOS-based scheduling
- RPC inter-core communication
- DMA audio pipeline
- Average current: <85 µA (99.9% sleep duty cycle)
Designed for Southeast Asian rainforest monitoring where:
- Power is limited
- Long-term autonomy is required
- High recall is critical
ARGUS performs edge inference directly on embedded hardware --- no cloud required.
- Two-tier ultra-low-power design
- ≥99% bird activity recall
- On-demand species classification
- Unknown species capture for dataset growth
- Hardware-aware ML architecture
MIT
Publication forthcoming.