Check out my latest release “Speculation and Imagination”, combining gentle melodic sequences with harsh, noisy feedback.
These tracks are an edit of my live analog synthesizer improvisation from a online “session” with painter Onozaki Takuya. Onozaki and I meet monthly for jam sessions over Zoom, him in Hanamaki and me in Connecticut, where he paints, and I play my synths, exchanging artistic ideas across our mediums.
If you happen to be around Toyko this week, Onozaki’s works, some of which were created in these sessions, are on display 2/7/25-2/16/25 at “Gallery Camellia”in Ginza.
Patching up a Karplus-Strong synth from scratch in Reatkor 6 Primary.
Karplus-Strong synthesis is a digital synthesis technique that simulates the sound of plucked strings by using a feedback loop to model the behavior of a vibrating string. Developed by Kevin Karplus and Alex Strong, this method generates resonant waveforms by feeding back a short noise signal through a filtered delay line with feedback.
In this video we build a simple K-S synth from scratch in Native Instruments Reaktor 6, exploring what happens based on the design decisions that we make. By the end of this video, you should understand both the theoretical concepts and practical implementation in Reaktor, giving you a unique tool for your music production arsenal.
Check out more intermediate Reaktor Tutorials here:
Video presentation I made for the 2024 “Explainable AI for the Arts” (XAIxArts) Workshop, part of the ACM Creativity and Cognition Conference 2024.
A lot of these points I’ve discussed elsewhere (see playlist below), but this quickie presentation brings together these ideas, focusing on the aesthetic potential of this approach.
Check out the complete playlist for more hands-on creation of neurons and neural networks:
Adding some subtle dimension to your synthesized instruments with early reflections.
Synthesized instruments, unlike recorded sounds, have never existed in the acoustic world. This means that these synthesized sounds are 100% direct signal. To add some subtle dimension to these synthesized sounds, then, we can craft some “early reflections” on these tracks.
Here, I demonstrate this concept using ChromaVerb in Logic Pro X.
0:00 Introduction 0:55 Understanding Reverb and Early Reflections 2:18 Creating Early Reflections 3:18 Project Setup 4:26 Early Reflections Aux Track 5:15 ABing on the Synth Track 6:19 Why Separate these from the Main Reverb? 6:40 ABing on the Whole Arrangement
Creating DAW-based feedback loops, then using side-chain compression to regulate them.
Here, working on a project with @SpectralEvolver , I show in Logic Pro X how we can use a compressor side-chained to a beat to control a feedback loop for some noisy, industrial sounding music that sounds evocative of the artist Emptyset. I found this was a great way to create a chaotic sound, but keep it under control (and out of the way of the drums).
0:00 Intro 0:29 The audio tracks 1:15 Side-chain compression 2:03 The feedback loop 3:13 Controlling the loop with compression 5:00 Emptset 5:13 Two aux tracks sending to each other 6:23 A note about time-based effects 6:50 Will it blow up?! 8:06 Closing, next steps
Finding and removing subaudio from sample files with a waveform editor.
Subaudio are frequencies below the range of human hearing (below 20Hz). These frequencies can sneak into our recordings, and work against us in a number of ways. If we can address subaudio in our samples, we can do ourselves a favor in the later stages of our mixing process.
0:00 Defining Subaudio 0:59 Example 1: Spotting Subaudio 2:04 Example 1: Doing the Math 2:50 Why Did This Happen? 3:11 Removing Subaudio with Parametric EQ 5:53 Example 2: Not Really Subaudio 7:27 Harmonics of Subaudio 8:31 Example 3: Trimming 9:15 Example 4: Bringing It All Together 10:16 Closing. Next Steps
Building a simple artificial neural network in Max/MSP for nonlinear, chaotic control of data-driven instruments.
I’ve talked before about data-driven instruments, and I’ve talked before about artificial neurons and artificial neural networks, so here I combine the ideas to use a simple neural network to give some chaotic character to incoming data from a mouse and joystick before converting into into MIDI music. The ANN (Artificial Neural Network) reinterprets the data in way that isn’t random, but also isn’t linear, perhaps giving some interesting “organic” sophistication to our data-driven instrument.
In this video, I work entirely with musical control in MIDI, but these ideas could also apply to OSC or directly to any musical characteristics (like cutoff frequency of a filter, granular density, etc.).
0:00 Intro 1:43 [mousestate] for Data Input 2:58 Mapping a linear data-driven instrument 7:19 Making our Artificial Neuron 15:27 Simple ANN 20:06 Adding Feedback 22:23 Closing Thoughts, Next Steps
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.
