Pd Patch from Scratch: Filter Pinging

Doing some filter “pinging” use the resonant [bob~] filter in Pd Vanilla.

Filter pinging is a synthesis technique where you sent a “pop” (i.e. an audible click) to a resonant filter to create a percussive plucking sound around that filter’s cutoff frequency. Since we’re in Pd Vanilla, the easiest way to get a resonant filter is with [bob~], the “Runge-Kutte numerical simulation of the Moog analog resonant filter.”

There’s no talking on this one, just building the patch, and listening to it go.

0:00 Setting up the filter
0:40 Filtering a sawtooth wave
1:35 Subaudio [phasor~]
2:04 Randomizing cutoff frequency each ping
3:33 Commenting the code
5:12 Oops

Pure Data introductory tutorials here:


Accelerometer-Controlled Analog Feedback (iPad, Max/MSP, and Eurorack)

Using the tilt of an iPad to control an analog feedback-based patch.

In this patch, I send OSC (Open Sound Control) data about the tilt of my iPad to Max/MSP on my computer, which converts this data into MIDI, and subsequently, on my synth, voltage. That voltage (changing based on the iPad’s tilt) opens up a VCA (voltage-controlled amplifier) which controls the level of the feedback.

The analog feedback loop here goes: ring-modulator to saturator to reverb to delay to low-pass filter to the VCA (which runs back into the ring-modulator).

More digital control of analog Eurorack here:

Patch from Scratch: Reaktor Feedback Loop

Building a dynamic feedback loop in Reaktor 6 Primary.

Here’s a simple patch based off the work of composer/engineer Jaap Vink from the Institute For Sonology, Utrecht. This ensemble is a feedback loop with a delay, a ring modulator, and a saturator (with a simple sine as a “trigger” to get things started).

Each pass through the loop, the signal is delayed, then ring-modulated, significantly changing the spectrum. This can devolve into noise rather quickly, but a soft touch can lead to some interesting evolving soundscapes.

There’s no talking on this one, just building the patch, and listening to it go.

More Audio Cybernetics and Feedback:

Pure Data Artificial Neuron Patch from Scratch

Patching up an artificial neuron in Pure Data Vanilla for some nonlinear mixing. There’s no talking on this one, just building the patch, and listening to it go.

An artificial neuron is basically just a mixer: inputs come in, and are weighted differently, modelling the dendrites of a biological neuron; then the mixed signal is transformed by an “activation function”, usually nonlinear, and output, modelling the axon.

Now, we can say that “learning” occurs when we adjust the weights (levels) of the inputs based on the output, but let’s not do that here, let’s just revel in our our nonlinear mix.

More details in my blog post here

0:00 Nonlinear Mixing and Artificial Neurons
1:17 Adding “Bias”
2:28 Neuron Complete
3:27 Automating the Weights
7:09 Adding Feedback
8:42 Adding Noise
9:58 Commenting our Code
11:25 Trying the ReLU Activation Function
12:04 Linear Mixing (with Hard Clipping)

Pure Data introductory tutorials here
More no-talking Pure Data jams and patch-from-scratch videos

No-Input DAW (Logic Pro X Feedback Loops & Sound Design)

Tutorial on “no-input mixing” in a DAW (Logic Pro X, in this case) for wild feedback-based sound design.


With a little knowledge of digital signal flow, we can easily set up an aux track in our DAW as a feedback loop–sending the track back into itself. Once we start adding effects, we can achieve new and unexpected sounds. This technique could be a way to generate some new sonic material, add some interest to a drum loop, or even generate vast, evolving soundscapes.

0:00 Intro / Casio Beat
0:39 Output to Aux Track
1:06 Feeding Back with a Bus Send
2:20 Adding Effects to the Loop
4:14 More Subtle Effects
4:58 More Extreme (Pitch Shifter)
5:17 Removing the “Input”
6:47 Talking through the No-Input Mixer
8:18 Closing Thoughts

More Logic Pro X tutorials:

This Fundamental Frequency Is an Illusion

All sound can be broken down into individual frequency components, and the lowest frequency component of a sound is called the “fundamental” (all the frequencies above that fundamental frequency are the “partials”). By cleverly setting the relationships of the amplitude and frequencies of the harmonic spectrum, though, you can trick your ear into hearing the pitch of a sound as an octave below the lowest frequency component.

Here, I’ve built a quick demo in Reaktor 6. Listen and see what you think.

More on additive synthesis here.

More Reaktor Tutorials here.

Pure Data Screaming Metal Feedback Loop

A simple digital feedback patch in Pure Data build from just delay, ring-modulation, and saturation.


Building on my digital feedback video from a few weeks ago, here’s a quick patch for setting up a dynamic controllable feedback loop in Pd Vanilla. I’ve set up a way to get things going with a little sine-wave beep, and you can hear that the feedback loop makes things pretty complex pretty quickly.

WATCH THOSE LEVELS!
It gets loud in the middle.

More no-talking Pd videos here.
More music and sound design with cybernetics and feedback.

Music and Synthesis with a Single Neuron

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).

Nonlinear Circuits “Dual Neuron” (Magpie Modular Panel)

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:

Three signals in, One out

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.

Hyberbolic Tan Function (tanh)

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!

Just one neuron

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?

We’ll save that for another day, though.

EDIT 05.18.22 – Taking it on the road!

Audio Feedback Loops in Digital: Cybernetic Music in Symbolic Sound Kyma 7

Building some feedback loops in the digital domain using Symbolic Sound’s Kyma 7.

In audio feedback loops, the output of the system is fed back into an input. We’re probably most familiar with this when we put a microphone in front of a speaker and we get the “howling” sound. Here, though, I’m intentionally building digital feedback loops in order to explore the sonic possibilities of these rather unpredictable systems.

In order to keep my feedback loop interesting, though, I need to keep it from dying away to silence, or blowing up into white noise. By considering the different processes we apply to the audio in the loop (are they adding spectral complexity or removing it?), we can try to make feedback patches that are dynamic and interesting over time.

0:00 The Continuum of Spectral Complexity
3:13 Staring with an Sine Wave in Kyma
4:45 Delay with Feedback
5:49 Building Feedback Loops Manually
8:40 Ring-Modulating the Feedback
11:20 Gain and Saturation
14:22 Exploring the Sound
16:16 Filter Bank
19:05 Jamming with the Patch
22:18 Thinking about Control
23:25 Performing the Sound
26:34 Feedback Loop with Reverb
28:10 Making it into IDM with the Chopper
29:22 So What? Next Steps

More Kyma Videos:

More audio cybernetics and feedback: