New Music! “Hanamaki Sessions 2023”

by SimonPosted on No Comments

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 Machine Learning Fail

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A failed attempt at machine learning for real-time sound design in Pure Data Vanilla.

I’ve previously shown artificial neurons and neural networks in Pd, but here I attempted to take things to the next step and make a cybernetic system that demonstrates machine learning. It went good, not great.

This system has a “target” waveform (what we’re trying to produce). The neuron takes in several different waveforms, combines them (with a nonlinearity), and then compares the result to the target waveform, and attempts to adjust accordingly.

While it fails to reproduce the waveform in most cases, the resulting audio of a poorly-designed AI failing might still hold expressive possibilities.

0:00 Intro / Concept
1:35 Single-Neuron Patch Explanation
3:23 The “Learning” Part
5:46 A (Moderate) Success!
7:00 Trying with Multiple Inputs
10:07 Neural Network Failure
12:20 Closing Thoughts, Next Steps

More music and sound with neurons and neural networks here:

Pd Comb Filter Patch from Scratch

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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:

Artificial Neurons and Nonlinear Mixing

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Talking through the concept of an artificial neuron, the fundamental component of artificial intelligence and machine learning, from an audio perspective.

I’ve made a few videos recently with “artificial neurons” including in Pure Data and in Eurorack, and, in this video, I discuss the ideas here in more detail, specifically how an artificial neuron is just a nonlinear mixer.

An artificial neuron takes in multiple inputs, weights them, and then transforms the sum of them using an “activation function”, which is just a nonlinear transformation (of some variety).

Of course just making a single neuron does not mean you’ve made an artificial intelligence or a program capable of “deep learning”, but understanding these fundamental building blocks can be a great first step in demystifying the growing number of machine learning programs in the 21st Century.

More music and sound design with artificial neurons:

Pure Data Clamping VCA with [clip~]

by SimonPosted on One Comment

Creating an ambient music machine in Pure Data Vanilla with a “clamping VCA” that adds subtle distortion, imitating the envelopes in Roland TR-808.

I made a clamping VCA in Reaktor a few weeks back, and now here’s another example in Pd. Normally, amplitude envelopes in synths are a control envelope on the amplitude of the signal. When we use a “clamping VCA”, though, instead of controlling the amplitude of the waveform, we clip it at the desired maximum envelope. This means, when the VCA is all the way up, it sounds the same, but during the attack and release, we’ll get the addition of subtle (or perhaps not-so-subtle) distortion to our waveform.

I use [clip~] in Pd to achieve this effect, stealing the idea from Noise Engineering’s “Sinclastic Empulatrix” module, which, in turn, stole the idea from from the Roland TR-808 drum machine’s cymbal envelopes.

More Pure Data Tutorials:

Clamping VCA in Reaktor 6 Primary

by SimonPosted on One Comment

Building a “clamping VCA” in Reaktor for subtle distortion, imitating the envelopes in Roland TR-808.

Normally, an amplitude envelope for your synths are just that: a control envelope on the amplitude of the signal. When we use a “clamping VCA”, though, instead of controlling the amplitude of the waveform, we clip it at the desired maximum envelope. This means, when the VCA is all the way up, it sounds the same, but during the attack and release, we’ll get the addition of subtle (or perhaps not-so-subtle) distortion to our waveform.

I use the “Mod. Clipper” in Reaktor 6 to achieve this effect, stealing the idea from Noise Engineering’s “Sinclastic Empulatrix” module, which, in turn, stole the idea from from the Roland TR-808 drum machine’s cymbal envelopes.

0:00 The “Mod. Clipper”
0:33 Clamping VCA
1:25 Simple Sine Oscillator
2:03 Mod-Clipping the Sine Wave
3:51 Standard VCA for comparison
4:58 Pulse Wave
5:41 Sawtooth Wave
6:34 Adding a Filter
7:35 Next Steps

More Reaktor 6 Intermediate Tutorials:

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

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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

by SimonPosted on One Comment

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: