Remoticon Video: How To Use Machine Learning With Microcontrollers

Going from a microcontroller blinking an LED, to one that blinks the LED using voice commands based on a data set that you trained on a neural net work is a “now draw the rest of the owl” problem. Lucky for us, Shawn Hymel walks us through the entire process during his Tiny ML workshop from the 2020 Hackaday Remoticon. The video has just now been published and can be viewed below.

This is truly an end-to-end Hello World for getting machine learning up and running on a microcontroller. Shawn covers the process of collecting and preparing the audio samples, training the data set, and getting it all onto the microcontroller. At the end of two hours, he’s able to show the STM32 recognizing and responding to two different spoken words. Along the way he pauses to discuss the context of what’s happening in every step, which will help you go back and expand in those areas later to suit your own project needs.

Continue reading “Remoticon Video: How To Use Machine Learning With Microcontrollers”

Let This Crying Detecting Classifier Offer Some Much Needed Reprieve

Baby monitors are cool, but [Ish Ot Jr.] wanted his to only transmit sounds that required immediate attention and filter any non-emergency background noise. Posed with this problem, he made a baby monitor that would only send alerts when his baby was crying.

For his project, [Ish] used an Arduino Nano 33 BLE Sense due to its built-in microphone, sizeable RAM for storing large chunks of data, and it’s BLE capabilities for later connecting with an app. He began his project by collecting background noise using Edge Impulse Studio’s data acquisition functionality. [Ish] really emphasized that Edge Impulse was really doing all the work for him. He really just needed to collect some test data and that was mostly it on his part. The work needed to run and test the Neural Network was taken care of by Edge Impulse. Sounds handy, if you don’t mind offloading your data to the cloud.

[Ish] ended up with an 86.3% accurate classifier which he thought was good enough for a first pass at things. To make his prototype a bit more “finished”, he added some status LEDs, providing some immediate visual feedback of his classifier and to notify the caregiver. Eventually, he wants to add some BLE support and push notifications, alerting him whenever his baby needs attention.

We’ve seen a couple of baby monitor projects on Hackaday over the years. [Ish’s] project will most certainly be a nice addition to the list.