Planet Arduino

At Arduino Day, I talked about a project I and my collaborators have been working on to bring machine learning to the maker community. Machine learning is a technique for teaching software to recognize patterns using data, e.g. for recognizing spam emails or recommending related products. Our ESP (Example-based Sensor Predictions) software recognizes patterns in real-time sensor data, like gestures made with an accelerometer or sounds recorded by a microphone. The machine learning algorithms that power this pattern recognition are specified in Arduino-like code, while the recording and tuning of example sensor data is done in an interactive graphical interface. We’re working on building up a library of code examples for different applications so that Arduino users can easily apply machine learning to a broad range of problems.

The project is a part of my research at the University of California, Berkeley and is being done in collaboration with Ben Zhang, Audrey Leung, and my advisor Björn Hartmann. We’re building on the Gesture Recognition Toolkit (GRT) and openFrameworks. The software is still rough (and Mac only for now) but we’d welcome your feedback. Installations instructions are on our GitHub project page. Please report issues on GitHub.

Our project is part of a broader wave of projects aimed at helping electronics hobbyists make more sophisticated use of sensors in their interactive projects. Also building on the GRT is ml-lib, a machine learning toolkit for Max and Pure Data. Another project in a similar vein is the Wekinator, which is featured in a free online course on machine learning for musicians and artists. Rebecca Fiebrink, the creator of Wekinator, recently participated in a panel on machine learning in the arts and taught a workshop (with Phoenix Perry) at Resonate ’16. For non-real time applications, many people use scikit-learn, a set of Python tools. There’s also a wide range of related research from the academic community, which we survey on our project wiki.

For a high-level overview, check out this visual introduction to machine learning. For a thorough introduction, there are courses on machine learning from coursera and from udacity, among others. If you’re interested in a more arts- and design-focused approach, check out alt-AI, happening in NYC next month.

If you’d like to start experimenting with machine learning and sensors, an excellent place to get started is the built-in accelerometer and gyroscope on the Arduino or Genuino 101. With our ESP system, you can use these sensors to detect gestures and incorporate them into your interactive projects!

Bespoke Electromechanical Instrument was built by Jay Harrison as part of a dissertation undertaken on the Creative Music Technology degree course at Staffordshire University. The instrument, running on Arduino Mega 2560 is designed  to allow each note to be independently placed in a space:

The project involved the creation of an electromechanical system capable of autonomously playing a bespoke Lithophone musical instrument. The underlying idea was to create a Lithophone that allowed the audience to literally step inside it, giving a unique spatial and acoustic surround experience. Designing an autonomous electromechanical system was thought to be the most effective and reliable to solution to achieving this.

The Arduino Mega 2560 was used to interface Max/MSP with the physical circuitry. Control messages/signals would be sent out of a Max/MSP patch using Maxuino, these signals would then be interpreted by the standard firmata sketch loaded onto the board and would go on to trigger and control the 24 rotary solenoids and 24 servo motors that work to produce the notes.

In the video below the instrument is arranged in a 24-foot surround configuration and the audience is invited to experience the instrument from within offering a unique spatial dimension to the Lithophone intended to completely envelop the listener:

[vtol] is quickly becoming our favorite technological artist. Just a few weeks ago he graced us with a Game Boy Camera gun, complete with the classic Game Boy printer. Now, he’s somehow managed to create even lower resolution images with a modified typewriter that produces ASCII art images.

As with everything dealing with typewriters, machine selection is key. [vtol] is using a Brother SX-4000 typewriter for this build, a neat little daisy wheel machine that’s somehow still being made today. The typewriter is controlled by an Arduino Mega that captures an image from a camera, converts it to ASCII art with Pure Data and MAX/MSP, then slowly (and loudly) prints it on a piece of paper one character at a time.

The ASCII art typewriter was recently shown at the 101 Festival where a number of people stood in front of a camera and slowly watched a portrait assemble itself out of individual characters. Check out the video of the exhibit below.

Filed under: Arduino Hacks

Cata Sopros is interactive sound installation running on Arduino Uno and created by Elas Duas, a multidisciplinary studio based in the city of Guimarães (Portugal). If you translate the title from portuguese it means: Breathe Catchers. In fact the project is a collective musical instrument made with paper windmills transforming the users’ breathe into sounds:

The windmills have inbuilt electret microphones that were connected to an Arduino Uno. The sensor data was then sent to MaxMSP and the sounds were played with Ableton Live. The video was shot at the cloister of the beautiful Alberto Sampaio museum in Guimarães, Portugal.

Enjoy the video:

[Igor Stolarsky] plays in a band called 3′s & Sevens. We’d say he is the Guitarist but since he’s playing this hacked axe we probably should call him the band’s Guitarduinist. Scroll down and listen to the quick demo clip of what he can do with the hardware add-ons, then check out his video explanation of the hardware.

There are several added inputs attached to the guitar itself. The most obvious is the set of colored buttons which are a shield riding on the Arduino board itself. This attaches to his computer via a USB cable where it is controlling his MaxMSP patches. They’re out of the way and act as something of a sample looper which he can then play along with. But look at the guitar body under his strumming hand and you’ll also see a few grey patches. These, along with one long strip on the back of the neck, are pressure sensors which he actuates while playing. The result is a level of seamless integration we don’t remember seeing before. Now he just needs to move the prototype to a wireless system and he’ll be set.

If you don’t have the skills to shred like [Igor] perhaps an automatic chording device will give you a leg up.

[via GeekBoy]

Filed under: Arduino Hacks, musical hacks

Arboration controls a projected landscape based on the musical input of the user.

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The Physical Equalizer is built upon a set of homemade flex sensors. When blocks are stacked on top of them, the volume of different instruments in the loop changes.

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Moritz Simon Geist has made a physical version of the famed TR-808 drum machine.

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