Pd Comb Filter Patch from Scratch

Building a comb filter in Pure Data Vanilla from scratch.

A comb filter is a filter created by adding a delayed signal to itself, creating constructive and destructive interference of frequencies based on the length of the delay. All we have to do is delay the signal a little bit, feed it back into itself (pre-delay), and we get that pleasing, high-tech robotic resonance effect.

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

0:00 Playing back a recorded file
0:35 Looping the file
1:00 Setting up the delay
2:08 Frequency controls for the filter
2:52 Setting the range
3:48 Automatic random frequency
4:25 Commenting the code
5:39 Playing with settings

More no-talking Pd patch from scratch:

Zoomscapes Updates

For the last few years, I’ve been messing around with internet-based, no-input feedback loops in collaboration with Will Klingenmeier.

What does that mean? Why would I do that? What does it sound like? All those questions are answered in the brief PechaKucha below:

Zoomscape Pecha Kucha – Understand it all in less than 8 minutes!

While I’m sure we’ll continue to mess with these ideas in the future, we’ve come to at least a short-term culmination of this project in a tape release of these experiments on bandcamp.

You can also retroactively join our “Tape Release Party” here:

Zoomscapes Tape Release Party from 2/5/23

To catch up on all of the previous experiments, check out this playlist:

Interactive Holiday Noise Machine

Doing some live processing of sleigh bells in Pure Data to create an “Interactive Holiday Noise Music System.”

Since it’s mid-December, let’s make some holiday music. If you’re sick of the standard cloying Muzak fare, though, you can make your own feedback delay sample-crushing interactive music system in Pure Data in an afternoon.

The main point here is getting a “trigger” from audio input crossing a loudness threshold. Once we have that trigger, we can use it to make changes in live-processing of a sound and trigger other sounds too. This is a simple idea, but its effectiveness is going to depend on what these changes are and how we play with the system.

0:00 Demo
0:26 Introduction / Goals
1:23 Input Monitoring
2:41 Direct (“Dry”) Output
4:08 Feature Extraction with [sigmund~]
6:55 Amplitude as Trigger
8:43 Triggering Changes in Delay
12:44 Sample-Crushing
17:03 Triggering an Oscillator
19:37 Oscillators into Harmony
23:35 Putting it all together
25:33 Closing Thoughts

More experimental Christmas music:

Elektrosluch and Stereo Microphones

Listening to electromagnetic radiation around the house using a homemade elektrosluch.

I was cleaning up, and found an “elektrosluch” that I made a few years back, and figured I’d dust it off and make sure that it still works. This is a device designed by LOM-Instruments that converts the vibration electromagnetic fields into sound (specifically vibrations of voltage that we can listen to through headphones, more info here ).

Make your own with this DIY article.

Thinking Beyond the ADSR, Other Envelope Generators in Reaktor 6 Primary

Adding envelopes to our synthesizer that aren‘t an ADSR.

ADSRs might be the envelope generators that we encounter most often, but they’re not the only way to shape our sound. There are a number of other musical ways to craft change in our synthesizer over time with these non-periodic TVCs.

Let’s check out what other options there are in Reaktor 6 primary.

More Reaktor 6 Beginner Tutorials here:

Pure Data Artificial Neural Network Patch from Scratch

Coding (well, “patching”) an artificial neural network in Pure Data Vanilla to create some generative ambient filter pings.

From zero to neural network in about ten minutes!

In audio terms, an artificial neuron is just a nonlinear mixer, and, to create a network of these neurons, all we need to do is run them into each other. So, in this video, I do just that: we make our neuron, duplicate it out until we have 20 of them, and then send some LFOs through that neural network. In the end, we use the output to trigger filter “pings” of five different notes.

There’s not really any kind of true artificial intelligence (or “deep learning”) in this neural network, because the output of the network, while it is fed back, doesn’t go back an affect the weights of the inputs in the individual neurons. That said, if we wanted machine learning, we would have to have some kind of desired goal (e.g. playing a Beethoven symphony or a major scale). Here, we just let the neural network provide us with some outputs for some Pure Data generative ambient pings. Add some delay, and you’re all set.

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

0:00 Demo
0:12 Building and artificial neuron
2:00 Networking our neurons
3:47 Feeding LFOs into the network
4:20 Checking the output of the network
5:00 Pinging filters with [threshold~]
8:55 Adding some feedback
10:18 Commenting our code
12:47 Playing with the network

Creating an artificial neuron in Pd:

Pinging Filters in Pd:

More no-talking Pure Data jams and patch-from-scratch videos:

Ihatov MU (無) : Noise Music at the Hanamaki “English Coast”

Screaming noise improvisation on 54HP Eurorack at the peaceful Hanamaki “English Coast” (花巻イギリス海岸).

There’s a feedback loop going here with spring reverb and ring modulation, plus quite a bit of contribution from the After Later Audio Benjolin V2.

More “MU” on the Hanamaki English Coast:

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!