The Meganome

[Update: Due to the nerd blog media explosion (which is great!), I've had to buy Bandcamp download credits so I can keep giving away my music. If you've enjoyed the videos and music and you'd like to support the creation of future r0b0 beats, you can buy Vio for iPhone and iPad. It's $2.99 and makes your ears feel really good. I use it for the vocals on my EP The Human Element]

A year and a half ago I decided I had to abandon my horn claw MIDI controller. It was a tough decision because there was a lot to like about the controller: gestural control of rhythmic density and beater velocity, zebra wood, and of course, horns. That controller is the first, and as far as I know, only device to put dead springbok into the service of beat-making, a distinction that has earned it pride of place on my bookshelf of discarded electronics. But in the end, what mattered was making music live, and the horn claw made that difficult. It monopolized my right hand and didn’t have enough buttons to trigger pitched instruments. Enter the Meganome.

The author, with horn claw and djembe machine.

Sensors

Inspired by the monome, which pioneered grid-based minimalist controllers in 2006, the core interface of the meganome is a grid of illuminated square buttons. I wanted buttons that made a definitive “CLACK” when struck, and I found them in Adafruit’s illuminated square arcade buttons. Unlike the famous MPC drum machine, these buttons are not velocity sensitive, but I was able to compensate partly for that deficiency by including a number of continuous controllers, including pressure sensors and endless encoders that control drum and synth velocity. The other continuous controllers include a proximity sensor and an XY joystick–the same model used in the Playstation controller.

Arduino Code

This video shot by Hal Ibaba Lovemelt of Playatta explains how the controller works to direct drum machines and synths better than I could in blog post, so what I want to discuss here is some of the problems I’ve encountered with the Meganome and offer tips to those who want to build something similar. If you are familiar with Arduino, most of the code for the Meganome should be fairly easy to parse. (You can download my code here. Right click and Save As). The code has a DEBUG flag, which you should set to true in order to communicate with the Arduino from the serial monitor. If you’d like the Mega to appear as a USB MIDI device, set the flag to false.

MIDI Communication

Getting the Meganome to function as a plug-and-play USB MIDI controller was tricky but worthwhile because it makes communication with MAX/MSP much simpler and more stabile. Making the Mega or any Arduino appear as a native MIDI device to your computer requires replacing the firmware on the Mega’s ATmega8U2 chip with new, MIDI-capable firmware from MocoLUFA. These two posts will get you started on doing that, but prepare yourself for frustration. Even if you get everything to work properly, every time you want to tweak your code you will have to first replace the MocoLUFA firmware with the original firmware so you can upload a new sketch. Once you’ve uploaded the new sketch, you’ll have to do the firmware switch in the opposite direction to re-enable MIDI communication. This process involves some command line tedium, and I recommend writing down the steps in a document so you don’t have to re-read those blog posts like I did. I’m not responsible if you brick your pricey Mega.

LED Drivers

I used TLC 5940 chips from Texas Instruments to drive the LEDs, and in my experience these chips work great when you’re using only one or two of them. When you start daisy-chaining more than a few chips, however, communication errors between chips occur. With the Meganome, this has the unfortunate side-effect that sometimes LEDs don’t light up when they’re supposed to. As you can imagine, this is especially aggravating during live performance. As a result, I’m going to move to using shift registers to control button LEDs. If you’re interested in using the TLC chip, the Arduino site has a good tutorial.

Shift Registers and Encoders

The shift registers have proven completely reliable in sensing button presses. The only failures to sense button presses have occurred as a result of my sloppy soldering. The Arduino site has a good tutorial on shift using shift registers for digital input.

The encoders from Digi-Key were more temperamental. They sent noisy signals to the Arduino, and code I found in online tutorial failed to suppress it. Eventually, I found a workable solution that involved using capacitors to filter the encoder signal and writing redundant sensing code that does everything it can to make sure it knows which direction the encoder is turning. The filtering circuit is diagrammed in the datasheet, and the sensing code is in the source I posted above.

Future Work

I’ve been inspired by virtuosic performances I’ve seen with Ableton’s Push controller to rethink how I lay out pitch space on the Meganome. The Push controller, another grid-based offspring of the monome, staggers scales across two rows, so one hand can play a triad, which makes playing traditional tertian harmonies much easier. That’s attractive. And I may also condense the drum controls so I can play all of the machines from one mode, instead of switching from one mode for bongos, djembe, and drum kit, and another for bells and shakers. Finally, wheels for the case. The meganome’s purpleheart case is beautiful, but it isn’t doing my back any favors. It’s that or build robot roadies.

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Smuggling African bell patterns into a mass-market, automatic techno app could be a prank pulled by a frustrated ethnomusicologist turned software developer, and part of me would like to believe that. But my hunch is that Figure’s predilection for off-kilter rhythms falls out of the mathematics of Figure’s interface design. That math traces its ancestry back to Euclid and has found applications, sometimes deliberate and sometimes not, in particle accelerators, string theory, Western scales, and African rhythm. What these cases and Figure have in common is patterns distributed as evenly as possible.

Imagine that you have 16 small boxes arranged in a circle and you are given five balls to place in the boxes. Only one ball can fit in a box, and the balls should be spread out as far from each other as possible, as though some repulsive force existed among them. If you follow these simple rules, there is only one pattern that can result. If we unroll the circle of boxes and lay them out in a line, they look like this:

[O][ _ ][ _ ][O][ _ ][ _ ][O][ _ ][ _ ][ _ ][O][ _ ][ _ ][O][ _ ][ _ ]

Where we start unrolling the circle of boxes is an arbitrary decision, so there are 16 variants of this pattern, each one starting at a different box, but in each case the pattern of distances from each ball to the others would be the same. OK, we have some balls in boxes, so what? If you imagine that each box represents a duration of one 16th note, and each ball is a clave note, you have the Bossa Nova pattern. The rhythmic foundation of Jobim and The Girl from Ipanema materializes out of a few simple rules. And if you move just one of the balls left or right and again interpet the pattern as clave strikes in a loop of 16th notes, you can generate the Cuban Son, the Gahu pattern of the Ewe, and the Rumba.

These bell and clave patterns demonstrate a quality that music theorists call maximal evenness, the property of events being evenly distributed (or nearly evenly) among a discrete set of locations. Much of what we know about clave patterns and the mathematics of maximal evenness comes from the research of Canadian computer scientist Godfried Toussaint, a polymath whose research spans “computer vision, visualization, computer graphics, computer-aided design, automated manufacturing, knot theory, polymer physics, and computational biology.” Since 2003, Toussaint has focused his research on the “computational musicology” of clave rhythms. Prior to Toussaint, ethnomusicologists such as Simha Arom and David Locke had offered insights into what makes certain repeated rhythms captivating, but Toussaint brought rigor, computational chops, and historical perspective to the topic, which brings us back to Euclid.

As Toussaint writes in his 2005 paper, Euclid’s Elements treatise, published around 300 B.C., describes a simple but non-trivial algorithm for computing the greatest common factor of two integers. Here’s how it works. Given two integers, subtract the smaller number from the larger until what started as the larger becomes zero or the larger becomes smaller than the smaller. If the larger reaches zero under (repeated) subtraction, you’re done. The greatest common factor is the smaller number. If the larger number becomes smaller than the smaller after subtraction but is not zero, swap the small number with the now even smaller number, and repeat the whole operation. Eventually, subtracting the smaller from the larger will reach zero, and you’ll have your answer. Toussaint summarizes the algorithm in pseudo-code:

Let m and k be the input integers with m > k.
EUCLID(m;k)
1. if k = 0
2. then return m
3. else return EUCLID(k,m mod k)

This algorithm can generate maximally even patterns like the Bossa Nova clave if we interpret the algorithm spatially. Instead of balls in boxes, let’s represent clave hits with a ’1′ and empty durations with a ’0,’ and to simplify matters, instead of 16 possible locations we’ll use 8. Let’s place our 1s and 0s in a line with the 1s on the left and the 0s on the right.

1 1 1 0 0 0 0 0

Now we apply EUCLID(5,3) and, when we subtract 3 from 5, we move three 0s behind each of the 1s. After moving these three 0s, we group them with their neighboring 1s in square brackets. And we’ll put square brackets around the remaining 0s to show that we now have five sequences–three sequences of [1 0] and two sequences of [0].

[1 0] [1 0] [1 0] [0] [0]

We started with eight singlet sequences, three of 1 and five of 0, and now we have five total, three of [1 0], and two of [0]. Applying the algorithm once again, this time calling EUCLID(3,2), and interpreting the process spatially by interpolating the smaller sequences with the larger ones, yields

[1 0 0] [1 0 0] [1 0]

If you treat each position as an eighth note and each 1 is an onset, congratulations, you’ve generated one of the most popular rhythms ever invented. This rhythm, Toussaint writes

is none other than one of the most famous on the planet. In Cuba it goes by the name of the tresillo and in the USA is often called the Habanera rhythm used in hundreds of rockabilly songs during the 1950’s. It can often be heard in early rock-and-roll hits in the left-hand patterns of the piano, or played on the string bass or saxophone [7], [15], [22]. A good example is the bass rhythm in Elvis Presley’s Hound Dog.

Don’t believe Godfried? Listen to the guitar (it’s easier to hear than the bass) in this live performance of Hound Dog.

Like Elvis, maximally even Afro-Latin rhythms live on, often in surprising places. Figure, the mobile dance music app from the Swedish software developer Propellerheads, uses maximal evenness as the guiding principle in generating its dance beats. The motivation for using maximal evenness wasn’t, I don’t think, an affinity for computational ethnomusicology; rather, some interpretive schema was necessary for converting input from a highly simplified user interface into specific rhythms, and the Euclid algorithm can do that. Figure was conceived as an app for the consumer market, not professionals, and consequently it tossed out the step sequencer interface that has been a hallmark of beat making apps because using a step sequencer requires some skill and it monopolizes the entire screen.

iPhone Step Sequencer

Figure for iPhone

Figure needed rhythm controls with the opposite characteristics: the controls had to be economically sized, and they couldn’t allow you to do something that sounded bad. The solution Propellerheads’ designers hit upon was to use a single control for each percussion instrument. This control changes the number of notes played by that instrument in every bar; it’s like a rhythmic density throttle. In the image at right, you see the controls for the kick, snare, hat, and cowbell, and the number of the circle represents the number of notes that each instrument can play in a measure (users have the opportunity to select only a subset of possible notes by pushing the buttons below the circle, but we’ll ignore this possibility to simplify matters).

The user can tell Figure how many notes to play in a measure, but the app has to decide which notes to play. It decides with the Euclid algorithm. When you tell Figure to play three snare hits per bar, it plays three hits distributed as evenly as possible among the 16 16th notes in a bar of 4/4. When you tell it to play five hits in a bar, it spaces those five hits out in the pattern we discovered in the balls-in-boxes exercise. Figure lines up beat 1 with a different part of the pattern, resulting in four dotted eighth notes followed by a quarter note, but the spacing is the same. It’s the Bossa Nova pattern begun on a different note. Every time you tell Figure to play X notes in a bar of 16 sixteenth notes, it calculates EUCLID(16,X).

Condensing the controllable rhythmic parameters to one number per instrument obviously saves space, but why do these maximally even rhythms sound good? I tried to answer that question in a paper presented at the 2008 International Society of Music Information Retrieval conference. (Full disclosure: I met Godfried Toussaint at the conference, and I thought he was a congenial as well as very smart guy). My argument was that maximally even rhythms in which the number of notes does not evenly divide the number of locations, for example 5 in 16 and 3 in 8, are easy to hear and re-hear in a variety of metrical contexts. Like the gestalt flip that occurs when you look at foreground-background illusions (is it a vase or is it two faces? etc.) the metrical ambiguity of maximally even patterns offers our brains opportunities to re-experience the same thing in a new way. That’s why these rhythms can stay interesting when repeated over and over.

That, anyway, was the theory. The paper didn’t set the world of computational musicology ablaze, so discount accordingly. What was interesting to me was that a mathematical theory of rhythm that is normally discussed at obscurantist academic conferences had been directly implemented in a mass market product. Furthermore, the success of Figure in entertaining millions of people who are not musicians offers further proof that these are indeed a special class of rhythms. I’ll close with a video I made a couple days ago of me using Vio with Figure. Enjoy the syncopated maximal evenness in the hi hat.

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… so much flashes through my mind when i hear the tapes of this album that if i could i would write a novel about it full of life and scenes and people and blood and sweat and love.

Ecology and narrative: those are the qualities in Bitches Brew that left me awestruck. The sensation that you’ve been sucked into a wormhole and deposited into an alien place with half-familiar beings who move about their lives–that impressed me and seemed so much grander than just expressing emotions. Why sing about your last breakup when you could render whole sonic worlds that abstractly allude to fantastical peoples and landscapes? Money, for one thing, and the patience of your friends for another, but Gleason’s imaginary novel remains a sort of lodestar for me.

This jaunt down memory lane serves as a long-winded way of saying I have some long tracks on the EP. Meteor Shower clocks in around thirteen and a half minutes and 8 Bit Mission ambles across the finish line at nine. Meteor Shower goes for that dramatic narrative arc in a big space–let’s call it the full Gleason–more than any other and is the only track to indulge in synthesizer improvisation. 8 Bit Mission does a head fake toward brofisting electro house before settling into what my friend who does make brofisting electro house derisively calls “African jazz.” 8 Bit Mission would be the first track to make African jazz with looped, distorted, transposed, vocoded vocals, but he’s not entirely wrong.

Squelch doesn’t do a head fake toward fist-pumping idiocy; it embraces obnoxious synths, bass drops, and beat repeats in all of their sublime stupidity. “The stupid sublime” would be my dissertation topic if I were kidnapped and forced into a cultural studies PhD program. The transcendent stupification of AC/DC guitar riffs, Skrillex wobbles, and Southern rap lyrics would be exhibits A through B in my argument that musical idiocy can bring us as close to the precipice of the infinite as earthquakes and mountain tops. It would at least be an interesting dissertation defense.

Quick Minute is a fast, all robo-percussion sprint through trap and footwork styles. My studio is located down the hall from a number of trap producers, and their crazy hi hats, big basses, and taste for ultra syncopation have seeped through the walls into my practice sessions. Quick Minute grew out of playing along with the noise bleed.

Erik Satie in a Djembe Drum drops the temperature for a few minutes of diaphanous chords cribbed from Erik Satie’s famous Gymnopedia No. 1. The Satie piece doesn’t appear directly; you hear its harmonies speak when hits from the djembe drum jostle a bank of resonant filters tuned to the frequencies in the piano piece. I carry the main melody with a distorted, flute-like preset in the vocal processor.

And now, the tech specs. The main controller I used in the production and performance of “The Human Element” is a custom MIDI controller that I call the Meganome. It was inspired by the monome and MPC controllers and consists of 84 square, illuminated arcade buttons, an XY joystick, two ebony pressure sensors, and a proximity sensor, all housed in a case of purpleheart and curly maple. An Arduino Mega is the brains of the controller, which connects to a laptop via USB. The buttons have ultra light springs, which make for fast action and playability. In synth control mode, scales are laid out across the Meganome’s rows with octaves along the columns, allowing for some unusual chord voicings and melodic leaps that would be difficult on the keyboard.

Most of the album uses radical vocal transformations, which are performed with a voice processor I created originally for my own use in Jazari and continued developing as it was incorporated into Vio, an iPhone app I’ve collaborated on with Audiofile Engineering. Sometimes the transformations are so radical that the results don’t sound like a human voice. The first sound in 8 Bit Mission, for example, is my voice run through what became Vio’s audio engine. The vocoding and spectral processing add a cyborg cast to the human voice and not, I hope, in a dull, lifeless way, but one that acknowledges that technology mediates everything we do and we may as well revel in it. That idea, I think, is the honesty behind the artifice of making yourself sound like a quartet of alien synthesizers.

The whole system is tied together by a big MAX patch running Java code in MXJ externals. The synths and effects are handled by MSP, and MIDI commands sent out from the patch are received by Arduinos in each of the machines. The arduinos trigger solenoids that hit drums that are individually miked, and the mikes send audio back to MAX/MSP for processing like frequency shifting, pitch shifting, or phasing. This system of processing drum sounds owes a lot to academic computer music, where it’s common to apply radical, drawn-out processing to acoustic instruments.

It’s taken four years of building, coding, practicing, and writing to create an album that I think stands on its own as music, and I’m glad to be able to share it with you. Thank you for reading, watching, and listening.

– Patrick

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The first track on The Human Element is getting a video treatment with thousands and thousands of particles rendered in Processing. This is a still from one of the clips I’ve generated today, which is a bit more subdued than the others. Most of the clips have a psychedelic, cortex-melting flair, but this one had a cooler, impressionist look that I like.

Update. Here it is:

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I haven’t put up a performance video in some time, and this is the first one that shows the MegaNome controller in action.

Minneapolis-based vocalist Aby Wolf is riding a wave of success with her Wolf Lords project with Grant Cutler. The sound of her voice pitch corrected to just intonation with the Sitar Hero preset is one the most beautiful sounds I’ve heard from the app.

Rapper and beat-boxer Carnage The Executioner discovered sounds in the Wormhole preset that were completely new to me.

OSO coaxed some beautiful harmonies out of the presets Tasmanian Gothic and Eau Claire while ad-libbing lyrics.

Other than me, all of the performers were completely new to the app when we shot these videos. They played with it for 15 minutes, and then we started rolling tape, which shows a couple things, I think. One, they’re really talented. Two, it’s pretty easy to get started with this app, which was an overriding goal throughout development. We didn’t want users to have to familiarize themselves with obscurantist controls labeled ASDR, and I think we succeeded. I’ll write more about the process when I have time, but for now, enjoy the videos, and if you like the app, please like the app on FB at facebook.com/VioApp or follower Vio on Twitter @vio_app.

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Dave Hickey: 09/17/2009 from MFA Art Crit on Vimeo.

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What makes something interesting is open to debate, which is exactly the point. Interesting work aspires to inspire discussion and models itself on discursive disciplines like politics, sociology, or musicology. Conservative art critics used to bemoan this conceptual and linguistic turn and long for the pure sensory pleasures of l’art pour l’art; partisans of conceptual work accused the conservatives of shilling for an art market that demanded pretty baubles and quashed social subversion. What art theorists argue about now I’m not sure because I stopped paying attention to bitter aesthetic feuds when I left academia, but what has become clear over years of listening to job and conference talks and applying for grants is how institutional and market demand for interesting work shapes artistic production. Interestingness, as much as pretty paintings, is a consumed good.

Interesting works, for all their cerebral trappings, are easily packaged and delivered in the marketplaces that matter for composers (I’m focusing on composers and academic music because it’s the world I know best.) A piece that performs data sonification by mapping train schedules to musical notes offers a nice “Aha” moment when you realize “Oh, there goes the F train. Neat.” These pieces fire the cognitive pleasure neurons like grasping the predictably counter-intuitive argument of a Malcolm Gladwell think piece. And because they can be grasped quickly, especially with a little prodding from a program note or verbal explanation, they are ideal for job talks and grant proposals. With just a little explanation and a short snippet of video, an audience can get the piece because the piece is reducible to its concept. Unlike some behemoth of absolute music devoted to its own formal development, interesting pieces make for great elevator pitches.

The composer Mark Applebaum recently gave a TEDx talk (what else) in which he defends interestingness as an aesthetic criterion against more traditional and, to his mind, boring categories. Applebaum is a very smart and funny presenter; watch the whole thing if you can.

Applebaum’s talk demonstrates just how compelling interesting works can be when you listen to someone talk about them.

His pieces play puckish games with the definition of music and offer whimsical, knowing fun for audiences with only the vaguest familiarity with classical music. Each excerpt, paired with Applebaum’s commentary, is a delicious 30 to 60 second morsel of brain food. They’re easy to get but are too clever in their conception to feel dumb. After watching Applebaum run through selections from his catalog, you have to wonder how anyone would talk about a piano piece, old or new, that just sounds good. That would just be, to quote Applebaum, “really boring.” And if it would be boring to talk about, and you need to talk about it to secure your professional livelihood, why write it at all? The savvy thing to do is create something conceptually interesting, ideally interdisciplinary, and then present the hell the out of it.

There are worse problems to suffer than an abundance of interesting works of music and art. Still, every time I hear an interesting data sonification piece or novel interdisciplinary collaboration, I’m reminded of the gap that separates them from the music that made the strongest impression on me when I was learning guitar and later composition. The sublimity of late Beethoven string quartets, the searing spirituality of A Love Supreme, the cortex-melting stupidity of AC/DC’s Back in Black (no seriously, more on that in the future)–you can’t describe any of these pieces as interesting. They have larger (and in the latter case dumber) ambitions. I wonder if any 16 year old has ever been motivated to take up an instrument or composing because he heard something that was so interesting he had just to learn how to make interesting music too. I doubt it. Let’s just please not let the dance music producers find out that their beats are supposed to be interesting.

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Here’s the slideshow for the talk: https://docs.google.com/presentation/d/1o2I8dQnhTKCgiakCF5px4p4KY_yonwDm-MazNML9Qq0/edit

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