Planet Arduino

Step sequencers are fantastic instruments, but they can be a little, well, repetitive. At it’s core, the step sequencer is a pretty simple device: it loops through a series of notes or phrases that are, well, sequentially ordered into steps. The operator can change the steps while the sequencer is looping, but it generally has a repetitive feel, as the musician isn’t likely to erase all of the steps and enter in an entirely new set between phrases.

Enter our old friend machine learning. If we introduce a certain variability on each step of the loop, the instrument can help the musician out a bit here, making the final product a bit more interesting. Such an instrument is exactly what [Charis Cat] set out to make when she created the After Eight Step Sequencer.

The After Eight is an eight-step sequencer that allows the artist to set each note with a series of potentiometers (which are, of course, housed in an After Eight mint tin). The potentiometers are read by an Arduino, which passes MIDI information to a computer running the popular music-oriented visual programming language Max MSP. The software uses a series of Markov Chains to augment the musician’s inputted series of notes, effectively working with the artist to create music. The result is a fantastic piece of music that’s different every time it’s performed. Make sure to check out the video at the end for a fantastic overview of the project (and to hear the After Eight in action, of course)!

[Charis Cat]’s wonderful creation reminds us of some the work [Sara Adkins] has done, blending human performance with complex algorithms. It’s exactly the kind of thing we love to see at Hackaday- the fusion of a musician’s artistic intent with the stochastic unpredictability of a machine learning system to produce something unique.

Thanks to [Chris] for the tip!

Even though machine learning AKA ‘deep learning’ / ‘artificial intelligence’ has been around for several decades now, it’s only recently that computing power has become fast enough to do anything useful with the science.

However, to fully understand how a neural network (NN) works, [Dimitris Tassopoulos] has stripped the concept down to pretty much the simplest example possible – a 3 input, 1 output network – and run inference on a number of MCUs, including the humble Arduino Uno. Miraculously, the Uno processed the network in an impressively fast prediction time of 114.4 μsec!

Whilst we did not test the code on an MCU, we just happened to have Jupyter Notebook installed so ran the same code on a Raspberry Pi directly from [Dimitris’s] bitbucket repo.

He explains in the project pages that now that the hype about AI has died down a bit that it’s the right time for engineers to get into the nitty-gritty of the theory and start using some of the ‘tools’ such as Keras, which have now matured into something fairly useful.

In part 2 of the project, we get to see the guts of a more complicated NN with 3-inputs, a hidden layer with 32 nodes and 1-output, which runs on an Uno at a much slower speed of 5600 μsec.

This exploration of ML in the embedded world is NOT ‘high level’ research stuff that tends to be inaccessible and hard to understand. We have covered Machine Learning On Tiny Platforms Like Raspberry Pi And Arduino before, but not with such an easy and thoroughly practical example.