Making Minty Fresh Music With Markov Chains: The After Eight Step Sequencer

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!

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Silicone And AI Power This Prayerful Robotic Intercessor

Even in a world that is as currently far off the rails as this one is, we’re going to go out on a limb and say that this machine learning, servo-powered prayer bot is going to be the strangest thing you see today. We’re happy to be wrong about that, though, and if we are, please send links.

“The Prayer,” as [Diemut Strebe]’s work is called, may look strange, but it’s another in a string of pieces by various artists that explores just what it means to be human at a time when machines are blurring the line between them and us. The hardware is straightforward: a silicone rubber representation of a human nasopharyngeal cavity, servos for moving the lips, and a speaker to create the vocals. Those are generated by a machine-learning algorithm that was trained against the sacred texts of many of the world’s major religions, including the Christian Bible, the Koran, the Baghavad Gita, Taoist texts, and the Book of Mormon. The algorithm analyzes the structure of sacred verses and recreates random prayers and hymns using Amazon Polly that sound a lot like the real thing. That the lips move in synchrony with the ersatz devotions only adds to the otherworldliness of the piece. Watch it in action below.

We’ve featured several AI-based projects that poke at some interesting questions. This kinetic sculpture that uses machine learning to achieve balance comes to mind, while AI has even been employed in the search for spirits from the other side.

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Blisteringly Fast Machine Learning On An Arduino Uno

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.

Foundations For Machine Learning In English (Or Russian)

We are big fans of posts and videos that try to give you a gut-level intuition on technical topics. While [vas3k’s] post “Machine Learning for Everyone” fits the bill, we knew we’d like it from the opening sentences:

Machine Learning is like sex in high school. Everyone is talking about it, a few know what to do, and only your teacher is doing it.”

That sets the tone. What follows is a very comprehensive exposition of machine learning fundamentals. There is no focus on a particular tool, instead this is all the underpinnings. The original post was in Russian, but the English version is easy to read and doesn’t come off as a poor machine translation.

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