Procedurally generated electronic music | Hacker News Reader

Procedurally generated electronic music

signalsandsorcery.com

107 pointsstevehiehn48 comments

Raymond Scott[0] (1908-1984) comes to mind.

He designed rhythm generators and automatic baseline sequencers. He died trying to complete building a monster electronic instrument called the "Electronium" of which only one incomplete and partially functional example exists, owned by Mark Mothersbaugh.

I only wish his devices were documented or understood such that simulators or reproductions could be built.

[0]: https://en.m.wikipedia.org/wiki/Raymond_Scott

[1]: https://youtu.be/0V2TZKcWnXE

[2]: https://youtu.be/o6VsZiNjjZE

As usual, the comments of HN are the real gold :-)

Thank you for sharing. I had never heard of Ray Scott before. The two clips you shared are mesmerizing. Sounds like the EDM that is being created today..crazy how much ahead of his day Mr. Scott's invention and consequently produced music was...

Wow, that is awesome. I never heard of that guy.

Scott was great. The Manhattan Research, Inc. compilation chronicles his electronic music work, and while a lot of it is outtakes and commercial jingles, it's fascinating.

Fun fact: Scott also had a jazz combo and, among other things, recorded Powerhouse [1], which became a cartoon staple.

[1]: https://www.youtube.com/watch?v=qaC0vNLdLvY

This is really, really good. Reminds me a bit of the 8 Bit Construction Set, which is some random thing I downloaded in college, and loved: https://www.youtube.com/watch?v=qsKZenP8vTE

It's interesting: some of the earliest European polyphony could be described as "procedurally generated"; the cantus firmus was assigned liturgically, and the descant could be determined by what we would today call an algorithm (sacred music was at this point considered science rather than a creative art). Plus ça change...

One of my official duties on Hacker News is to remind people, at times like this, of "S2 Translation", a 1995 work by The Shamen which translated the coding sequence of a gene into music:

https://synapticism.com/notes/s2-translation-an-early-work-o...

It's a piece which could perhaps be revisited in the light of much improved membrane protein crystallisation techniques:

https://www.rcsb.org/pdb/explore/explore.do?structureId=4PIR

Agree.

Music is by and large about structure. What made it worth listening to was this structure combined with human imperfection in the details and once in a blue moon melodies and harmonies that sound like they are there without being actually played.

Of known composers perhaps Bach was at the time the most mathematical of them.

Within the next decade I believe algos will be able to write hit's. Already today a lot of the things we like to hear is really using technology to create soundscapes and hooks we would never be able to create as humans.

My bet is we will see DAWs that essentially offer composers shortlisted options to choose from. In my SignalsAndSorcery project I am really just using machine learning to find composition options that make sense in the give context (based on my data set). A DAW could just keep presenting a series of options to choose from and almost anyone could be a composer.

I'm currently working on a DAW, and this is definitely the direction we'd like to head. In our previous version, we added some basic chord progressions. The next most likely chords are suggested on statistics from popular music. But I'm stuck on where to take it next. I have some ideas for a melody generator, but I don't really know anything about machine learning, etc. When you are ready to implement you API, I'd be very interested in talking to you. Send me an email at roland@tracktion.com

I'm interested in this; mind if I tag in too?

is it possible to use a set of music samples and through software find what piece of music is most representative of that set of music?

like when you take a bunch of pictures of people and create a phase most representative of the set or which one of the set is most representative

@FigBug Cool. I'll ping you when/if I get something interesting stood up.

Yup agree completetly.

Here's a winning demoscene production, 256 bytes, on a Commodore 64.

https://www.youtube.com/watch?v=sWblpsLZ-O8

Technical discussion here: https://linusakesson.net/scene/a-mind-is-born/

Amazing sound and very detailed explanation, but the theory behind it is actually quite simple: music contains a lot of nested repetition, from the periodicity of a sampled wave to the overall structure of a song. Thus it is possible to express a lot of it succinctly with the equivalent of nested loops.

Another demo from him with a similar theme, but with completely custom and even more limited hardware: http://www.linusakesson.net/scene/bitbanger/index.php

Along similar notes, synth one-liners explored:

http://countercomplex.blogspot.co.at/2011/10/algorithmic-sym...

It blows my mind to think people could do anything with that little RAM. I'm the author of this project & it chews up about 32gb of ram to pump out one track.

Well, yours is using significantly more advanced music sources and makes much more music.

Reminds me of using cellular automata to generate music: http://tones.wolfram.com/.

Academia is significantly behind the times in this area

Artists like Autechre and related links in this thread have been doing this for years using Max/MSP, to a much greater effect (albeit it's apparently a hybrid where the humans are at the "control panel" of the generative engine guiding it in the direction they want)

https://www.youtube.com/watch?v=wdKIJHXzPkk

Years ago I remember listening to algomusic on the Amiga. Every so often I do a search to see if anyone has made a version that works on Linux, but with no luck. This is getting there, but still feels a bit rough around the edges.

A quick search found the the author of AlgoMusic has moved on to a new project

https://www.soundhelix.com

Some of the examples sound even better. That's my evening sorted.

The soundhelix examples sound really good.

Oh this is very cool. The structure reminds me of Matthew Brown's "Music for Shuffle" project: http://musicforshuffle.com/2014/04/06/session1/

Who owns the copyright on procedurally generated music?

Mostly curious since some of these sound better than the ambient tracks you get in games. Even better, you could constrain the parameters of the generation to have each instance of a game generate different, but thematically similar, procedural music.

> 503.03(a) Works-not originated by a human author.

> In order to be entitled to copyright registration, a work must be the product of human authorship. Works produced by mechanical processes or random selection without any contribution by a human author are not registrable.

Possibly nobody, but it depends how much input the program requires to generate the output. Just press a button and a song comes out -- can't be copyrighted. User has to enter a lot of parameters to generate output, user gets get copyright.

Just press a button and a song comes out -- can't be copyrighted.

I wonder: what would it take for a machine-generated work to be considered a work derived from a human-generated work? While the machine-generated work still wouldn't be copyrightable[0], would that prevent others from using the machine-generated work without an appropriate license?

(IANAL.)

[0] «To be copyrightable, a derivative work must incorporate some or all of a preexisting “work” and add new original copyrightable authorship to that work.» https://www.copyright.gov/circs/circ14.pdf [PDF]

> without any contribution by a human author are not registrable.

Is there really no contribution of a human author -- the programmer? He wrote the source code, and supposedly imposed artistic qualities in the music that are themselves copyrightable.

I agree that the user can't receive copyright since she didn't have any creative input.

This was actually much better than I expected. Wondering what kind of neural net can do this, and what are the outputs. It sounds a bit "phasey" as if there is an IFFT step.

So its not just a single neural net. Its a bunch of different ML algorithms chained together. I'm essentially automating a 12(ish) piece electronic band. So first I have a ML algorithm that makes a chord progression. Then another that makes a bassline from the chords. Then another that makes a beat from the bassline & the chords and so on. At each step it uses the selected patterns to predict the next layer to be added. So to answer the question directly you can use RNN for some timeseries stuff. To decide if rhythm pattern are compatible I found success with CNN, random forests, and support vector machines. I even found you can make reasonable chord progressions just using N-grams. What I'm trying to say is I found I had to make an entire audio production pipeline that uses ML at each step of the way.

Are you planning to describe all that in more detail? I skimmed the 4 blog posts you published, but it's still not clear to me how the music is being generated.

Also, why did you decide to separate the "layers"? Technically, an RNN trained on similar music should be able to generate all "layers" simultaneously.

I will hopefully add more detail eventually. When I say layer I am taking about a music layer like a melody not a layer of neurons. Most of the music is midi rendered with vst synths or sound fonts.

Yes, that was what I meant when I said "layers" as well. RNN can generate all layers at once (more than one note per time step for a single instrument, and more than one instrument per time step overall). Basically what I'm saying is if you train it on such complex music, it will learn to generate it.

Ah!, I think I understand what you are saying. I'm not sure if you used a NN before but you will notice that there is an exponential relationship with the number of inputs and the size of the training set required. Because I don't have millions of perfect training data I need to break down the problem and make ML models to deal with specific problems. For example predicting if a drum beat is compatible with a bassline etc.

What exponential relationship? Can you point me to any papers about it?

It's actually quite easy to generate polyphonic (classical style) music with an RNN: just predict the entire note frequency range of one time step as a binary encoded vector. But yes, I can see how for multiple instruments, the sparsity of inputs might require more training samples.

I'm wondering if the "layered" approach has some advantages, perhaps more control over some aspects of the music?

Ya, there is no official relationship. From my experiments the more inputs you have the larger the dataset required. I do not know what is best way to do this but the layered approach is how humans compose music so it made sense to try that. I'm not sure I agree that its easy to generate polyphonic music. I would like to be wrong but when you say "easy" I don't think you're referring to music that follows a harmonic structure or factors in timbre. Most music I hear follows structure with repeating themes etc. Not just a stream of conscious series of notes.

Well, I just tried googling "rnn generated music": https://youtu.be/A2gyidoFsoI?t=1m44s Sounds like a pretty good polyphony, but of course, it could be just overfitting on Bach training samples.

Here are a couple of more sophisticated (and original) pieces: https://soundcloud.com/user-95265362/op-1-for-piano-solo-in-... https://soundcloud.com/user-95265362/op-21-for-piano-solo-ge...

Those do sound amazing! My goal is to create interactive music. If you noticed in my UI it can change at any point. In order to do that I think I need to premeditate the harmonic structure. i.e chord progression. I suspect that those RNN are not conforming to a predetermined harmonic structure. I doubt if you ask it say generate a piece following Dmaj9/Amaj/Bmin/Bmin it will understand that but I dont know.

if you ask it say generate a piece following Dmaj9/Amaj/Bmin/Bmin it will understand that

Dmaj9/Amaj/Bmin/Bmin will be the seed sequence to the RNN. Of course, the network might ignore the seed, or if it's overfitting it will produce a chunk of the training sample which has this sequence in it, but you can definitely specify the starting point (and you usually do with RNNs).

In order to use RNN in the way you suggest I think I would have to create a very large data set with annotated chord progressons. Which is something I would like to do but it would take a very long time to create the training sets.

No need to annotate the chords. If you start from a midi file, you just encode the piece as a binary matrix (time range x frequency range). It gets trickier if different instruments are not separated because you would want to specify different instruments as separate channels (so your matrix becomes time range x frequency range x number of channels), but even this could be automated with ML (e.g. using image segmentation methods).

The most time-consuming part is finding enough training compositions with labels (such as composer, genre, mood, style, etc), and data cleanup.

Fascinating, thanks!

What do the icons mean? I can't figure it out.

(project author here) Ya, sorry, its not clear at all. About 6 months ago I got the system working sorta ok, but i found it a bit boring. So I thought maybe if the algorithm generates N interchangeable variations associated with an emotion I could maybe make the music interactive. So the icons are going to represent a different emotion that a user can switch to at any point. Obviously its not really work very well. This site is ultimately a tool for me to test switching between variations. The end goal is not a website but to expose a music generating api that could be integrated into say, VR/game or something that might want interactive music.

Procedural music is really hard to get right.

The breaks are unpredictable, kinda dope.

jesus christ, BEWARE ITS LOUD

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