The $15 "Ear" That Listens for Unwanted Drones

Every technology seems to have two sides to it. Take the internet, for instance. This global network lets us communicate with anyone, anywhere, at any time in an instant — but it also exposes us to endless privacy breaches and hours of wasted time on social media. Similarly, artificial intelligence can give us a big productivity boost. However, it also is used to commit fraud and create deepfakes.

Likewise, drones can be a double-edged sword. On one hand, they have proven themselves to be very useful for applications such as monitoring crop health or delivering life-saving medical supplies to remote areas. On the other hand, this same technology may be used to covertly spy on unsuspecting individuals.

Batear is a small and inexpensive device designed to address this specific concern with drones. Rather than using a complex and expensive computer vision-based approach, Batear keeps things simple and inexpensive so that anyone can use it. It requires nothing more than an ESP32-S3 microcontroller and a MEMS microphone to alert you to the presence of nearby drones.

This low-power hardware works because of a clever signal processing pipeline. Instead of relying on computationally heavy Fast Fourier Transforms (FFT), the system uses the far more efficient Goertzel algorithm to detect specific audio frequencies associated with drone rotor harmonics. This allows it to focus only on the frequencies that matter, dramatically reducing memory usage and processing demands — an ideal fit for a resource-constrained microcontroller.

The device continuously listens for sound through an I2S MEMS microphone and analyzes the incoming signal in small frames. By comparing tonal energy at target frequencies against overall broadband noise, Batear can determine whether a drone is likely present. When this ratio exceeds a predefined threshold for several consecutive frames, the system triggers an alert.

There is no need for an internet connection, cloud processing, or subscription service. This makes Batear not only cost-effective — coming in at under $15 in hardware — but also privacy-friendly and suitable for remote or off-grid deployments, such as farms or rural properties.

While early tests using prerecorded drone audio have been promising, the project’s creators emphasize that real-world performance will depend heavily on environmental factors like wind, background noise, and distance. As a result, they are seeking contributors to help refine detection thresholds and improve accuracy through field testing.

Whether you want to build your own, or help test and refine Batear, all the info you need is available on GitHub.