- How to Adjust X and Y Axis Scale in Arduino Serial Plotter (No Extra Software Needed)Posted 2 months ago
- Elettronici Entusiasti: Inspiring Makers at Maker Faire Rome 2024Posted 2 months ago
- makeITcircular 2024 content launched – Part of Maker Faire Rome 2024Posted 5 months ago
- Application For Maker Faire Rome 2024: Deadline June 20thPosted 6 months ago
- Building a 3D Digital Clock with ArduinoPosted 11 months ago
- Creating a controller for Minecraft with realistic body movements using ArduinoPosted 12 months ago
- Snowflake with ArduinoPosted 12 months ago
- Holographic Christmas TreePosted 12 months ago
- Segstick: Build Your Own Self-Balancing Vehicle in Just 2 Days with ArduinoPosted 1 year ago
- ZSWatch: An Open-Source Smartwatch Project Based on the Zephyr Operating SystemPosted 1 year ago
“Listen to temperatures” with TinyML
Can we “listen” a difference between pouring hot and cold water?
As you see in the video you can do it, but why? It is mentioned that the change is due to complex fluid dynamics reasons. Beyond the scientific investigation, the question we asked ourselves was: is this ability to “listen to temperatures” something that can be replicated using Artificial Neural Networks? We then tried to create an experiment using TinyML (Machine Learning applied to embedded devices).
We used very different water temperatures, with a range of 50 ° C between them. (11 ° C and 61 ° C); for each sample the listening time was the time taken by the glass to be filled (from 3 to 5 seconds). We are interested in capturing the sound of water only during the pouring process. The sound was recorded from the same digital microphone (sampling frequency: 16KHz) and stored as a .wav file in 3 different folders: cold water sound (“Cool”); sound of hot water (“Hot”); no sound of water (“Noise”).
The acquired dataset (via Arduino Nano 33 BLE Sense) was uploaded to Edge Impulse Studio, where it was preprocessed, the neural network (NN) model was trained, tested and deployed on an MCU for a real physical test (an iPhone was also used for live classification).