Contributing

Current Status

Batear is functioning as a flashable baseline and has been successfully tested against prerecorded drone audio. We need real-world testing! Acoustic drone detection in real environments depends heavily on distance, wind, background noise, and drone type.

Areas We Need Help

If you have a micro drone, a soldering iron, and some free time, we would love your help:

• Real-world threshold calibration per drone type
• Noise filtering improvements
• ML-based detection (ESP-NN / TFLite Micro)
• Multi-gateway mesh networking
• Battery/solar power optimizations

Getting Started

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

See the Build & Flash guide to set up your development environment.

📊 Datasets & Benchmarking

To ensure the reliability of our DSP pipeline, we maintain a dedicated repository for high-fidelity acoustic samples:

👉 batear-io/batear-datasets

This repository contains: * Real-world Flight Data: Recordings from various drone models (e.g., DJI Mavic series). * Interference Profiles: Bio-acoustic signals (bats, birds) and environmental noise for stress-testing SNR thresholds. * DSP Tools: Python scripts for resampling and spectrogram visualization.

Contribute your data: If you have successfully captured drone or bat activity using Batear, please consider contributing your .wav files to our datasets repo via Git LFS.