Creating a Visualisation of Classroom Noise Levels – Lessons Learned

  Over the past two weeks, I’ve been working on creating a digital twin-style visualisation of classroom noise levels using Python, IoT sensors, and a Raspberry Pi. It’s been both rewarding and challenging, with some key lessons along the way:

🔹 Network challenges in institutional environments

Deploying an IoT device at an institute is very different from using it at home. Eduroam requires specific network access and IP configuration, unlike a simple home WiFi connection. As a temporary workaround, I configured my mobile hotspot to use the same SSID and password as my home WiFi — a creative but short-term fix. Connecting properly to Eduroam is still on my to-do list.

🔹 Sensor limitations

The GY-MAX4466 microphone module revealed its limitations: within a 3m radius, it was not sensitive enough to capture meaningful changes in noise levels. This highlighted the importance of sensor selection when designing monitoring systems for real-world environments.

🔹 Data visualisation experiments

Despite the sensor’s limitations, I explored different ways of visualising sound data:

1. A 3-tier gradient overlay that changes color based on noise levels.

2. A color legend on the side of the display, giving users clear reference to the dB scale.

3. Importing background images (e.g., classroom layout) into the Raspberry Pi to make the visualisation more user-friendly.

🔹 Exploring Node Red as an alternative UI

While Python + Matplotlib worked for initial prototyping, I also experimented with Node-RED Dashboard and UI templates. Writing HTML was intimidating at first, but the big advantage here is the potential for two-way communication — meaning not just visualising sensor data, but also sending commands or feedback back to the devices.

✨ These two weeks reminded me that technical progress isn’t always about everything working perfectly — sometimes the biggest lessons come from troubleshooting, testing limitations, and exploring alternative approaches.

Next step: testing better microphones and solving the Eduroam integration challenge! 🚀