For people who suffer from hearing loss or other auditory issues, maintaining situational awareness can be vital for keeping safe and autonomous. This problem is what inspired the team of Lucia Camacho Tiemblo, Spiros Kotsikos, and Maria Alifieri to create a small device that can alert users to certain household sounds on their phone.
The team decided to incorporate embedded machine learning in order to recognize ambient sounds, so they opted for an Arduino Nano 33 BLE Sense. After recording many samples of various events, such as a conversation, knocking on the door, the TV, a doorbell, and silence, they fed them into a tinyML model with the help of Edge Impulse’s Studio. The resulting model was able to successfully differentiate between events around 90% of the time.
Beyond merely outputting the recognized audio to a serial monitor, the team’s firmware also allows for the results to be sent over Bluetooth® Low Energy where a connected smartphone can read the data and display it. The mobile app contains three simple buttons for accessing a list of sounds, certain settings, and a submenu for managing the connection with the Arduino.
You can read more about this accessibility project here on Hackster.io.