A3EM (Animal-Borne Adaptive Acoustic Environmental Monitoring) is a low-power, machine-learning-enabled sensor platform that rides on wild and captive animals to observe their environment and behavior — adapting in real time to what it hears instead of just recording everything and sorting it out later.
Most acoustic wildlife monitoring today relies on stationary recorders and offline processing after the fact — an approach that doesn't scale, can't react in real time, and often exhausts battery and storage long before it captures the events that matter. A3EM instead puts a small, adaptive, machine-learning-capable sensor directly on the animal, so the system can learn what's worth keeping as it happens.
A3EM is an ambitious effort to bring embedded machine learning, resource-aware firmware, and field-tested hardware together into a platform that makes animal-borne acoustic sensing practical at scale. These six goals define what we hope to achieve by the end of the project — much of it still ahead of us.
We aim to build embedded ML that learns and classifies acoustic events in real time on the animal itself — adapting to new and unexpected sounds rather than relying on predefined event types.
We envision tools that let domain experts validate, relabel, and give feedback on system performance, continuously improving the network without exhaustive pre-labeling.
We're pursuing model compression and quantization efficient enough for deep learning models to run for months on a coin-sized power budget aboard a wearable device.
We're working toward multi-objective optimization that decides what to store, transmit, or discard based on event importance, remaining power, storage, and network availability.
A working hardware/firmware prototype, refined through real-world use — with field testing already underway across seven species on three continents.
We hope to develop a NetsBlox-based summer camp curriculum that introduces programming through wildlife conservation technology, broadening participation in STEM.
The platform moved to digital PDM microphones after tracing a cold-weather recording artifact to the analog front end, and firmware optimization cut average current from 7.9 mA to roughly 2.1 mA — shifting the deployment limit from battery life to SD card capacity.
Alongside ongoing savanna elephant, bighorn sheep, and caribou work, the project added first-time deployments on forest elephants with the World Wildlife Fund (Central African Republic) and brand-new collaborations on brown bear with the Scandinavian Brown Bear Research Project (Sweden) and white-lipped peccary with Osa Conservation (Costa Rica).
"Animal-Borne Adaptive Acoustic Monitoring" appeared in the Journal of Sensor and Actuator Networks (2025), detailing the hardware, firmware, and unsupervised adaptive-filtering approach. See publications →
A week-long acoustic monitoring workshop for 53 participants in Thailand, plus a second Nashville high school summer camp, and a funded WildLabs proposal to bring the platform to a broader community of ecologists.