New Music! “Hanamaki Sessions 2023”

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.

In this setup I have my 54HP Eurorack (which can be battery powered if I want to play on top of a lookout tower somewhere), and my Arturia DrumBrute Impact. I do mixing with a little Mackie mixer, and recording with a Zoom H4N (which lets me record sound from the microphones at the same time as the line inputs).

Last year, some might remember, I went around with just the Eurorack synth (with some different modules in it–a benjolin in particular) and recorded my three-track “Ihatov MU” album. This year’s sessions were a fun extension of those ideas.

Perhaps I should do some performing out in New England in the next few months.

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:

Kyma 7 Soundscapes (More Internet Feedback Loops with Spectral Evolver)

Using the latency from videoconferencing software as a delay for feedback loops, this time with Kyma 7 processing the signal at both ends, creating (noisy) evolving sonic textures.

During the pandemic, conferencing software quickly became a required part of work and education culture.

Of course, this technology’s ability to keep us connected has been and important part of keeping people safe, but we’ve also discovered the quirks of this mode of communication. Being bound to this remote interaction inspires curiosity about its potential for collaborative creativity. Musicians have know for a while about the issues of internet latency in coordinating remote ensembles, but what if, instead of attempting to recreate the conditions of a traditional performance in this new medium, we embraced the “space” created by this conferencing software?

In this performance, the audio signal is sent between the two Kyma systems, creating a feedback loop.

Feedback loops, such as when we put a microphone close to a speaker, emphasize the resonant frequencies—the imperfections—of a system. As we know, the audio of conferencing software is an imperfect connection, with latency, filtering, and audio compression artifacts.

This conferencing-software feedback loop, then, emphasizes these imperfections, bringing out the character of this communication medium as an emergent soundscape.

More explanation of these pieces here:

Check out Will’s channel 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:

Patch from Scratch: Analog Feedback Loop in Eurorack

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.

Previously, I’ve shown how to do the same thing in Pure Data, In Kyma, and in Reaktor.

Oh, and you can control it with an accelerometer too!

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:

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