Turning a single cycle of a recorded sample into a wavetable for Kyma oscillators.
When composing music with samples, it’s worthwhile to explore all of the musical opportunities in that sample–reversing it, timestretching it, granulating it, etc. Along those same lines, you can take a wavetable fro a sample and use it in your oscillators, so, instead of using the usual sawtooth, square, or sine waves, you create an oscillator that has a timbral connection to the sampled material.
Here, I show how to take take two vowel sounds from a vocal sample–an “ah” and an “oh”–and cycle them in a Kyma oscillator, creating unique timbres that blend with the original sample and its processing.
0:00 Intro / Why? 0:41 Finding a Single Cycle 3:14 Changing Duration to 4096 Samples 4:16 Cycling the Wavetable in an Oscillator 6:33 Making a Different Oscillator Wavetable 9:21 Implementation Example: Chords 11:49 Adding Vibrato 14:08 SampleCloud Plus Chords
In this performance, Python listens to live audio input from the bass, and, based on models trained with the dataset, sends out data to Unity3D and Kyma. Unity3D creates the visuals (the firework), and Kyma processes the audio from the bass.
First, though, the dataset used for training was collected from several pianists in the US and UK. As pianists played, we recorded multiple aspects of their performance: audio, video of their hands, EEG, skeletal data, and galvanic skin response. After playing, pianists listened to their own performance and were asked to record their state of “flow” over the course of the performance. All of these different dimensions of data, then, were associated over time, and so neural networks can be trained on these different dimensions to make associations.
This demonstration uses the trained models from Craig Vear’s Jess+ project to generate X&Y data (from the skeletal data), and “flow”, from the amplitude of the input. These XY coordinates, “flow”, and amplitude are sent out from Python as OSC Data, which is received by both Unity3D (for visuals) and Kyma (for audio).
In Unity, the XY data moves the “firework” around the screen. Flow data affects its color, and amplitude affects its size. Audio in Kyma is a bit more sophisticated, but X position is left/right pan, and the flow data affects the delay, reverb, and live granulation.
As you can see, amplitude to XY mapping is limited, with the firework moving along a kind of diagonal. Possible next steps would be to extract more features of the audio (e.g. pitch, spectral complexity, or delta values), and train with those.
Applying this data trained on pianists to a bass performance (in a different genre) does not have the same goals music-generation AI such as MusicGen or MusicLM. Instead of automatically generating music, the AI becomes a partner in performance. Sometimes unpredictable, but not random, since its behavior is based on rules.
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.
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.
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:
How to create an envelope follower in Reaktor 6 Primary and set your synth to automatically follow the amplitude of drums.
An envelope follower is a device that converts the amplitude envelope of an audio input into a control signal. Once we have that control signal, we can use it to control whatever we want. We can make the amplitude of an oscillator follow the amplitude of the input, or we could move the cutoff frequency of a filter, panning, etc.
Building the envelope follower is rather straightforward, just two steps: of rectifying and then low-pass filtering. In this video I walk through the process, and then show a few different applications.
Talking through bidirectional OSC (Open Sound Control) in my 2018 piece Baion (倍音), that I perform on a custom-built interface, “the catalyst”.
I’m performing my 2017 piece “Baion” this week, and I thought it was a good chance to revisit some of the mechanics of the piece, specifically the communication between the different elements–the custom interface, the Kyma timeline, and the game built in Unity3D. In this video, I go through how the musical work emerges from the bidirectional OSC communication between software.
ROMplers are synthesizer that create sounds through samples stored in ROM (Read-Only Memory). So, unlike “samplers” they are unable to record and manipulate new samples.
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
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:
