I’ve collected and edited some recordings I made with my “DAWless” mobile rig in Japan this summer.
It’s been interesting try to set something up that has the flexibility that I want, while still being portable enough not to take up too much space (and weight) in my luggage. Of course, as it’s often said, limitations can often lead to greater creativity.
Patching up an analog feedback loop in Eurorack with some generic modules.
I don’t do a lot of videos talking about Eurorack for two main reasons:
(1) I’ve actually only been doing Eurorack for a couple years now, even though I’ve been doing digital synthesis and sound design for decades, and
(2) I don’t want my videos to be about any particular piece of hardware that you need to get (as always, I’m not sponsored by anyone).
But, the patch I put together in this video could be done by any number of modules, all I have is a sine wave, a ring modulator (multiplier), a reverb, a filter, and a limiter/compressor/saturator (anything to stop hard clipping). Put them together, feed them back, and you have some dynamic, analog generative soundscapes.
Recently, I’ve been hooked on the idea of neurons and electronic and digital models of them. As always, this interest is focused on how these models can help us make interesting music and sound design.
It all started with my explorations into modular synths, especially focusing on the weirdest modules that I could find. I’d already spent decades doing digital synthesis, so I wanted to know what the furthest reaches of analog synthesis had to offer, and one of the modules that I came across was the nonlinearcircuits “neuron” (which had the additional benefit that it was simple enough for me to solder together on my own for cheap).
Anyway, today, I don’t want to talk about this module in particular, but rather more generally about what an artificial neuron is and what it can do with audio.
I wouldn’t want to learn biology from a composer, so I’ll keep this in the most simple terms possible (so I don’t mess up). The concept here is that neuron is receives a bunch of signals into its dendrites, and, based off of these signals, send out its own signal through its axon.
Are you with me so far?
In the case of biological neurons these “signals” are chemical or electrical, and in these sonic explorations the signals are the continuous changing voltages of an analog audio signal.
So, in audio, the way we combine multiple audio source is a mixer:
Now, the interesting thing here is that a neuron doesn’t just sum the signals from its dendrites and send them to the output. It gives these inputs different weights (levels), and combines them in a nonlinear way.
In our sonic models of neurons, this “nonlinearity” could be a number of things: waveshapers, rectifiers, etc.
In the case of our sonic explorations, different nonlinear transformations will lead to different sonic results, but there’s no real “better” or “worse” choices (except driven by your aesthetic goals). Now, if I wanted to train an artificial neural net to identify pictures or compose algorithmic music, I’d think more about it (and there’s lots of literature about these activation function choices).
But, OK! A mixer with the ability to control the input levels and a nonlinear transformation! That’s our neuron! That’s it!
In this patch, our mixer receives three inputs: a sequenced sine wave, a chaotically-modulated triangle wave, and one more thing I’ll get back to in a sec. That output is put through a hyperbolic tan function (soft-clipping, basically), then run into a comparator (if the input is high enough, fire the synapse!), then comparator is filtered, run to a spring reverb, and then the reverb is fed back into that third input of the mixer.
Now, as it stands, this neuron doesn’t learn anything. That would require the neuron getting some feedback from it’s output (it feeds back from the spring reverb, but that’s a little different) Is the neuron delivering the result we want based on the inputs? If not, how can it change the weights of these inputs so that it does?
How to receive and parse OSC (Open Sound Control) messages in Pure Data Vanilla for real-time musical control.
Open Sound Control, like MIDI is a protocol for transmitting data for musical performance. Unlike MIDI, though, OSC data is transmitted over a network, so we can easily transmit wirelessly from our iPhones or other devices. Another, difference, though, is that OSC messages don’t have standard designations (like MIDI “Note On” or “Note Off”), so we need to set up ways to parse that data and map it to controls ourselves.
Here, I go over the basics of receiving and parsing OSC data in Pure Data Vanilla, setting us up to make our own data-driven instruments.
0:00 Intro 2:46 [netreceive] 4:07 Sending OSC Messages 5:28 [oscparse] 6:02 Data! 7:11 [list trim] 8:09 [route] 9:03 [unpack] 9:46 Using the Data for Musical Control 13:52 Recap (Simplified Patch) 14:55 Explanation of Opening Patch
Talking about ideas of live electronic performance of electronic music using USB Controllers, Max/MSP, and Eurorack.
Here, I walk through how you can use a USB joystick to MIDI synthesizers (like my Eurorack modular) using Max/MSP as a “translator.” Information from the joystick and its buttons comes in on the [hi] (“human interface”) object, and we can shape that data and pass it out a MIDI data to whatever we want.
In this way, we can give ourselves nuanced control of our musical performance, enhancing our electronic music instruments.
0:00 Introduction 0:35 Generative Music and Feedback 1:31 Human Agency in Musical Systems 2:18 Devices for Human Interface 3:05 Today’s Goals 3:36 The [hi] Object 5:36 Looking at the Data 6:25 Isolating the Data with [route] 7:34 Converting the Numbers to MIDI 10:10 2D Piano 11:18 Sending MIDI to the NiftyCase 15:45 Controlling Effects (Wavefolder and Filter) 17:54 A Note about Resolution 18:49 Adding an Amplitude Envelope 19:58 Quick Recap 20:46 More Sophisticated Interactions of Data 23:04 Conclusion, Next Steps
A mess of Eurorack CV feedback that’s not random. It’s chaotic!
This instrument creates chaotic synthesized music that I interact with using four knobs. The music that this synthesizer creates is not random. It is determined by a set of “rules” created by the different components interacting with each other. However, because each of these modules influences and is influenced by several others, the interconnected network of interactions obfuscates the rules of the system. This leads to the instrument’s chaotic, incomprehensible behavior.
As with all chaotic systems, though, if it were possible to understand all of the different components and their relationships, and do complex enough calculations, we would be able to predict the outcome of all of our interactions.
Patch notes: ….Uh…. I just kept patching things back into each other, and this is where I ended up.
Building a resonant EQ in Reaktor Primary, taking inspiration from the Serge Resonant EQ’s unevenly-spaced frequencies and nonlinear controls.
In my regular journeys across the internet, I came across the Random*Source Serge Resonant EQ, a reissue of the resonant EQ from the Serge Synthesizer, and became a bit taken with its implementation and ideas. $400 is a bit too much for an impulse buy, so let’s see what we can do in Reaktor.
Even if we don’t end up with something that sounds perfect, we can use this as an opportunity to think more about subtractive synthesis, and talk about “parametric support” in our control schemes.
0:00 Purchase Your Way to Music Proficiency! 0:43 Random*Source Serge Resonant EQ 1:14 What’s interesting about this? 2:59 Disclaimer 3:22 Reaktor Primary Peak EQ 5:00 “Boost” vs. “Resonance” 5:53 Making Selectable Sound Sources 8:18 Throwing in an Oscilloscope 8:49 Starting the Resonant EQ Macro 9:28 Creating a Single Band 11:24 Level Controls to Avoid Clipping 13:13 One Knob for Resonance and Boost 14:28 “Funny Math” 21:13 Recapping the Flow / Fine Tuning 22:49 Duplicate! (for each frequency) 23:23 Setting the Frequencies 25:09 Adding a ByPass Switch 25:53 Sound Test 27:14 Saturator 28:04 Waveform Variance Across Instrument Range 29:38 Feedback 35:30 Next Steps