One example of more general EDM levels of abstraction for me might go
1a. this tune needs to play with space (stereo/reverb) more 1b. we need some interesting background sounds
therefore
2. we need an interesting, wide background sound to start at bar 32 and end at 64
Dropping in something from a sample library to achieve that wouldn't satisfy me as every step of constructing that sound is following other rules of abstraction e.g. how should it fit with the existing tonal balance? with the existing rhythm? etc. But at the same time the process is 1% inspiration 99% perspiration - it's following unwritten almost-rules which would be fascinating to capture in an algorithm if I could. They wouldn't have to be completely general rules, just my own personal ones.
A related (light hearted) thing I wrote on EDM abstractions a while back https://omnisplore.wordpress.com/2017/11/25/kolmogorov-compl...
Ha, no, you can input a great deal more than a single bar into them. They were designed to let users store a variety of patterns, which can then be assembled into a traditional intro-verse-chorus song structure, if you so wish.
909: 16 steps
303 bass: 5 steps
303 lead: 3 steps
I tried many other approaches involving 5 people driving a live looping rig but the above was worked best for me, which I guess made my act a 2010 equivalent of the guy wearing the burgandy velvet suit in the hotel bar ;-)
Maybe put a row of buttons at the top: "I am [Loving it] [Grooving] [Fine] [A Bit Bored] [About to Quit]". Then use that (plus browser data and window-close data) to train ML models to maximize engagement. Or perhaps to hook it up to facial expression recognition.
When I watch friends DJ, there's this great feedback loop between how the crowd's behaving and what they're putting on. They're clearly using the music to achieve certain mental states among listeners. I'd love to see how well that can be done algorithmically.
So many of the selections that great DJs make are curveballs and surprising choices. This is less true in more samey genres, but true pioneering selectors will tend to surprise you with their taste and juxtapositions in a way that's hard or maybe even impossible to really capture in an algorithm.
Put another way, even in something as simple a song selection, the weirdness and contingencies in human creative decision making is a feature not a bug.
The efficiency and cleverness of algorithms can create create great powerful recommendation engines but there's nothing like the idiosyncrasies of a person as a curator.
The combination it came up with that they then tried selling on a food stall was chocolate on pizza. It wasn't to everybody's taste but some liked it, with one describing it as "weirdly delicious".
It doesn't seem completely ridiculous that if an AI could be trained to recognise patterns of music that worked well together that it could analyse a large corpus of recorded music and come up with surprising mixes that you wouldn't think would work, but do.
Many restaurant chains sell chocolate on their pizza base as a dessert; chocolate is also known to complement many savoury dishes as a 'secret ingredient'.
Of course, there's always the possibility algorithmic composers could do even better than humans. Clarke told a story about that in 1957: https://en.wikipedia.org/wiki/The_Ultimate_Melody
Text here: https://archive.org/details/1957-02_IF/page/n71/mode/2up?vie...
That would be enough to construct a robot DJ that could track the variations it was making, and rate them for 'better/worse'. Then you just make a cool-looking puppet to store the camera in, that can move around and possibly wave an actuator like a proper DJ, and the rest is machine learning.
There was an interesting youtube video I watched, "All my homies hate Skrillex" that said that the evolution of agressive Skrillex-style "dubstep" over the older, more introspective dubstep was a result of smoking being banned indoors in UK clubs.
This made the smokers have to leave every now and then, which forced the DJs to have to play more agressive, instantly impactful music to draw their attention when they returned.
https://www.youtube.com/watch?v=-hLlVVKRwk0
I guess the video maker knows what he's talking about, he's the one who was at the clubs and clearly has great knowledge about the genre.
He said that he didn't care either way about the smoke, but personally I'd be much more likely to stay in a club with clear air than one filled with horrible smoke.
Another theory about the changing for more aggressive beats: maybe the dj's themself were affected and got angry having to wait for their next cigarette?
There are lots of games that do this kind of thing at a rhythm level, like Necrodancer and more recently BPM [1], but I think there'd be the potential to do a lot more with it than just rewarding beat-alignment with a pre-cooked solo that blazes over top of the base track.
As an example, I've become really interested in the fact that tempo and meter are taken as constant, grid-like structures in pretty much all existing music practices (even within, say, Gamelan, though there you do get a lot of pushing and pulling). Typically, you have polyrhythms and polymeters to fit more interesting patterns into the fixed grid, and accelerando and ritardando to adjust the rate of grid traversal, but that's about it. Hardly anyone applies algorithmic/geometric thinking to the grid itself — likely because more complex rhythmic foundations would make human performance nigh impossible.
To explore this space, I created my own little generative music system that takes a handful of simple motifs (a la Riley or Reich) and stacks them into a recursive temporal structure, which is then pushed logarithmically toward a tempo of 0 or infinity. There are some rules so that the "performers" only play at comprehensible scales, and that pitch modulation keeps the piece interesting to a listener. You can listen to it here, if you'd like: https://ivanish.ca/diminished-fifth/
I'd love to see more folks working on tools to make these sorts of theory-stretching ideas easier to access and explore. For instance, I've really been struggling with how to hand-compose music that can fit within a nonlinear/recursive structure of time. Existing tools like Tidal or Max/Pd were built to support the existing theory. I think we need new tools that allow you to design the theory itself.
> tempo and meter are taken as constant, grid-like structures in pretty much all existing music practices
I don't think that's remotely true.
Well, you're in good company. This goes back for many decades. Here is a summary of many attempts during this time: https://www.amazon.com/Algorithmic-Composition-Paradigms-Aut.... Really convincing composition started to appear only recently using transformers, e.g. https://openai.com/blog/musenet/. The present solution is a rather primitive one in comparison.
I coded up some jungle music not long ago: https://www.youtube.com/watch?v=GPan4gRSwZs&t=79s
I'm working on a procedure to modify the drum breaks in a conventional way, meaning I have to think less about keeping them interesting while live coding.
0: https://www.youtube.com/watch?v=SmwW5kBfltk&list=PL1yYEMwtFZ...
Back in the early days, I think CSound[0] was the big software, not because it can make especially interesting sounds compared to more "musical" software synths, but because it's a proper programming language which gives people the freedom to do these higher level abstractions.
In the hardware world, a lot of this developed out of arpeggiators and analog sequencers. I remember years ago I had a "P3" sequencer[1] which implemented a lot of these sorts of algorithmic pattern generaton tools - you could do things like quantize to a scale, then set a sequence of percentages that themselves impacted the likelihood of a particular note being played. I see there is a new version of this sequencer[2] too.
Lots of analog sequencers provide similar features, and if you have a modular setup you can ramp the tempo way down and have them control chord progressions or swells instead of 16th note patterns. Pretty sure this is how a bunch of live ambient music was done back in the day.
That was a fairly niche corner of electronic composition, but even in the mainstream of 20 years ago stuff like the Emu Proteus sound modules featured pretty advanced programmable arpeggiators where you could essentially write the skeleton of a musical sequence and then modify what notes of it actually ended up getting played by deciding which original notes to start from. I always came at this more from that minimal/techno/sequence-based side, but then I went to a wedding and saw a wedding singer playing an "accompaniment" keyboard which showed the other side - entire chord sequences and backing instrumentation getting generated in real-time based just on what chord the keyboard player chose to hit with their left hand. It's surely only a small step forward from there to being able to input a higher level algorithm that could develop a whole song.
[1] https://www.soundonsound.com/reviews/sequentix-p3
[2] https://www.sequentix.com/shop/cirklon-hardware-sequencer
If the author is reading this, Would love to hear about the quirks related to audio playback in browsers/devices you've found so far in developing this.
https://www.danieldavis.com/generative-design-doomed-to-fail...
For me the interesting part is not just in getting a computer to do all the work but using it as a compositional aide. You can think of it at writing music at a higher level of abstraction. Rather than writing a score you can write the rules of the score and use a stochastic process to spit out permutations for example. Eg imagine writing a program that could spit out Eric Satie-esque melodies.