#algorithmic composition

Between Lines — IMAGE→SRT (photos that sing)

I’ve added another new sister-tool to Between Lines: Image → SRT. Upload a still image, and it writes a subtitle file whose timings and “words” are derived from the picture. Feed that SRT into Between Lines and you get a playable score.

Below: a short render of Beksiński’s 1973 “Night Creeper” using my “Ritual Dread” settings, plus the, SRT output, mosaic preview and the app screenshot.

How it works (quick)

Grid the image into rows × columns (like a page: left→right, top→bottom).

Average each cell to a single shade/colour.

Map that value → a note:

grayscale: darker = lower pitch / longer sustain; light = higher; pure white can be treated as silence (invert for the opposite).

colour (hue) mode: hue picks pitch region, brightness can gate silence.

Timing: each cell = a fixed duration (e.g., 0.12 s). Optional row gap adds breath between lines—very phrase-like.

The tool writes two things:

an .srt file (the “score”), and

a mosaic preview PNG showing the exact grid the music came from (optionally with grid lines / silent cells marked).

Then open the SRT in /Between Lines/ and choose scale, stereo width, chord pad, reverb, etc., to match the image’s mood.

Pipeline: Image → SRT → /Between Lines/ → WAV (+ Mosaic)

Why SRT again?

SRT is the project’s “limbo layer.” Everything—speech, music, images—passes through it. Timing + minimal text become a neutral score that can be re-interpreted: seen (spectrograms), heard (sonification), or re-written. It’s the gap where meaning suspends… and forms.

Case study — Night Creeper

I ran the painting through Image→SRT (70×40 grid, 0.12 s/cell, hue with white→silence) and sonified it with a slow, heavy preset:

Scale: phrygian

Note band: 90–520 Hz

Articulation: 0.95 (legato)

Sustain (×): 2.8

Stereo width: 0.75

Caption chord pad: 0.08

Floor (drone): 0.05 @ 50–85 Hz

Reverb: 0.22 • Saturation: 1.03

Result: a crawling, heat-haze drone that fits the sodium-orange, rusted dread of the piece.

Knobs that matter

Rows / Columns — resolution of the “reading.”

Seconds per cell + Row gap — pace and phrasing.

Silence threshold (+ invert) — which parts of the image become rests.

Mapping — grayscale or hue.

Word-length range — acts like sustain inside /Between Lines/.

In /Between Lines/ — scale (chromatic, phrygian, aeolian, dorian, lydian…), stereo width, caption-chord pad, smoothing, reverb, saturation.

Where this is heading

All the tools are converging into a single space: Between Lines Studio — a desktop (or web) app where SRT is the spine, and you can go:

Audio → SRT (beats/bars/onsets)

Image → SRT (grid reading)

Text → SRT (write your own score)

then SRT → Music / Spectrogram / Image mosaic, and (for me) onward into signal // noise.

Upload a photo. Get a composition.

Working with Tidal Cycles and integrating its JavaScript offspring, Strudel, with Csound, has been very interesting and invigorating. You can see some of what I have been doing here.

Just recently, I noticed that Victor Lazzarini has created an online live coding environment for Csound taking off from the p5.js editor, although he has not (yet?) published the code.

As an algorithmic composer, I have long considered that the code of an algorithmic composition is the score of the composition. And it would follow that live coding, which involves writing code during the performance, would be something like improvisation.

But live coding is still code! So, in a sense, it is also composition.

So, what's the difference between a composition that results in a fixed medium, and a composition that is partly improvised?

The root meaning of "compose" is "put together." Musical composition is the art of putting together elements of music to create a new work.

The difference between algorithmic composition and live coding, as I see it, is that "composition" results in a new work that has a relatively fixed form such that one can hear it all over again and will, because one wants to hear the piece all over again.

Lines are blurred because of course, live coding sessions, or any other improvisations, can be recorded. And then there is a fixed medium that can be heard all over again.

But I think the distinction has been clarified. Composition intends to create a fixed work that is worth hearing again.

I have recently completed several algorithmic composition projects involving Common Lisp. These include:

Bringing Drew Krause’s code into my csound-extended repository as the nudruz system, and making it usable for other people besides Drew by creating a nudruz.asd system definition. Drew’s talk is well worth a look, it is what inspired me to do this stuff.

Implementing a foreign function interface for Csound in OpenMusic.

Embedding the ability to run Lisp code, including both the Lisp version of Common Music and Drew’s code, in my CsoundAC algorithmic composition system, written in C++. This is based on Embeddable Common Lisp.

There are things that I do not, or at least did not, like about Lisp. And there are things that I do like. But, now that I have moved on from just getting Lisp example pieces to work, to actually composing my own pieces in Lisp, I am finally beginning to “get” Lisp.

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I just took a peek at recent literature in the field of algorithmic music composition. Almost all work now is concerned with applications of artificial intelligence.

In my own work as an algorithmic composer and author of music software, I use AI in the form of ChatGPT every day. I use it to prototype code, to summarize literature, and to critique my own concepts and algorithms. And I will keep using it, because it speeds up my work.

Nevertheless, I have nearly zero interest in actually using AI to compose.

That is because I feel, based on both understanding and experience, that AI is not capable of the level of musical originality to which I aspire.

Much of the literature on AI composition that I see is about:

Composing music for use without having to pay a composer, e.g. for games, ads, and so on.

Musicological research, often with commercial applications.

I have no interest in either of these things. I am simply trying to use algorithms to compose the kind of music that I would like to hear, but that does not yet exist. Again, I don’t think AI is up to this (yet?). That leaves me with the following approaches:

Using the phenomenon of computational irreducibility as exemplified in fractals, chaotic attractors of dynamical systems, and so on. This in turn can be done:

By manually tweaking the algorithm to produce music that gets closer and closer to what I want to hear. Most of my music that I actually like has been done this way.

By interactively exploring a map of the parameter space for such algorithms — parametric composition. So far this is mostly a concept, because it is computationally very expensive.

Using a custom language with a recursive structure and terse vocabulary, so far just Lindenmayer systems, as this permits me to effect with simple edits changes throughout a piece and on all scales at once.

SuperColliderで音をポーポー出せるようにした　音響はパラメータ渡せるようにするつもり

I’ve been working with generative artificial intelligence (AI here) now for several years, trying to understand a bit about how it works, and following discussions by those much more knowledgeable and more concerned about it than I.

My main uses of AI have been to jumpstart my solutions to programming problems, and to answer questions about many different things in a better way than mere search engines. I also use image generating AIs to produce illustrations for some of my favorite science fiction novels.

I have come to some tentative conclusions.

As a result of working with Menno to get CsoundAC working as ReaScript in Reaper, I have come to appreciate some of the pros and cons of doing algorithmic composition in Reaper projects.

I will have more to say about this in the near future….

In the meantime, here is my first take on the pros and cons.