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The funny thing is that the only really good ones are the first few where they claim its just random noise. The later ones just sound like a crappy radio. With

The funny thing is that the only really good ones are the first few where they claim its just random noise. The later ones just sound like a crappy radio. With images the technique works because we like looking at the dense artifacts. Millions of dog heads essentially copy and pasted onto any appendage that looks like it should be a head. It looks like drugs and overwhelms in the same way. If you just take white noise and throw it through a tuned filter bank (which is in essence and in final effect what they are doing here) then you just get crappy audio. The more standard and successful use of NN in composition is the use it on pitch series and compositional forms. Like feeding it all of Beethoven and then getting it to generate similar compositions. That's been going on for decades. You can do it with that kind of data. But the thing about pop and electronic music is that the easily machine observable elements are not very interesting. Listen to the 4/4 kick and snare pattern in the video. Its boring as hell. (Other tracks can be just a kick and snare and they are amazing and we celebrate them as classics and play them for 20 years. Machines will never understand why) What's great and essential are things like spatial relationships between elements in the mix: how does the surge of the compressed synth/guitar cause the beat to tumble outward and stir you up ? after a series of peaks in a synth melody then the next time it pulls back creating a space that pulls at your heart strings. you create a negative space that the listener goes into. playing with listeners expectations based on what songs, conventions and tropes they already know and respond to.

sweezyjeezy 11y ago
The image stuff works because we have found a way to model a good prior for it : convolution layers are basically enforcing some positional invariance and locality constraints on what our model believes the world looks like. Without this very strict prior, image recognition with neural networks just wouldn't really work. We haven't found a way to enforce a good prior for temporal data like sound yet.
- TheOtherHobbes 11y ago
  I realised a while back a lot of computer music is really computer science music - it's people who know something about computers but not much about the art of music, playing with relatively trivial algorithms to create music-like results. There's also the academic musical equivalent - music professors using stock faddy techniques like serialism or (currently) number and group theory. It's not that this is an impossible problem. It's more that the set of people who can code machine learning algorithms and understand music theory and are creative enough to invent new algorithmic techniques and to create more-than-listenable music is incredibly small - double figures, if that. So progress in non-trivial computer music has been incredibly slow. The DSP side has been far more successful, because DSP is - in most ways - a much simpler problem.
  - crucialfelix 11y ago
    The reason for it has a lot to do with how Academia works. You need to write papers and produce innovative works. If you don't then you won't get funding or advance your career. > or (currently) number and group theory. That was Xenakis back in the 1950s ! Xenakis is the exception. He's really the foundation of academic computer music and his music is amazing and moving and his compositional concepts are still being hacked out by music programmers today.
  - skwosh 11y ago
    I somewhat agree about computer music, though it appears to be an extension of the process driven composition that has been part of western (art) music for a while now (e.g. modulation). Sometimes I wonder if linguistics has more to offer composition than algebra (speculation, as I know next to nothing about (non-CS) linguistics). > It's not that this is an impossible problem. It's more that the set of people who can code machine learning algorithms and understand music theory and are creative enough to invent new algorithmic techniques and to create more-than-listenable music is incredibly small - double figures, if that. Are you pursuing something like this? Or know anyone who is? This is one of my main interests (alongside better interfaces for composition and well, making music). I'm actually back at University for my second degree to study this sort of thing. If you have a blog or anything I'd be interested to find out...
  - ThomPete 11y ago
    Music is (dis)harmonies over rythmic patterns. There isn't anything inherently artistic about humans that computers can't replicate with time even the ability to compose an original song. That is besides the lives of humans and their appearance and history which is important but not the only factor. The irony is that musicians are actually striving for, but failing at, reaching the perfection level that computers have. And so for computers to sound more human like they have algorithms that make them more "sloppy" What composition algorithms lack is not the ability to compose like humans but a life that will give them angels and a story. Then again a lot of music is really formulaic anyway and computers are used for most of it. There is nothing in a few years that will hinder some sort of computer star to be born. But it's probably never going to connect with us the same way another human can. Not for now at least.
- crucialfelix 11y ago
  I think that music is not just the sound and not just the observable elements. It literally moves your emotional system around. We get shivers, we get horny, our mind shuts down and we get swept away in memories. That's not because of any specific arrangement of machine-observeable sound objects. > on what our model believes the world looks like Its not even doing that. Those images are just simple 2D fields of pixels with color data. It has no idea about any worlds. The spatial cues are way off, but we understand art (impressionism through messy expressionism, glitch) so we forgive it. And because they chose images with puppy eyes and we like puppies. Take away the animal elements and everybody would say the images were boring crap.
- murbard2 11y ago
  It helps but it's not necessary. Schmidhuber obtained state of the art performance on MNIST with a very vanilla deep net (no convolutional layer, no pooling, nothing fancy, just fully connected sigmoid units)
  - sweezyjeezy 11y ago
    MNIST is really easy, it's not a useful benchmark any more.
    - murbard2 11y ago
      Sure, but the fact that you get state of the art result without a convolutional prior ought to at least support the argument that the prior is not necessary
      - sweezyjeezy 11y ago
        You can get something like 5% error rate on MNIST with a well tuned linear classifier, it's not comparable with ImageNet. Note that the computer vision techniques used before convnets used things like SIFT features, which are another way of imposing a (sort-of) prior. I do believe that some sort of strong prior is necessary for the problem.
aflinik 11y ago
I think I like your musical taste.
rndn 11y ago
> Its boring as hell. Machines will never understand why That statement seems to be overly strong. Perhaps we are far from having a machine that can figure out high level aspects of a song on its own, but I don't think it's implausible that with some more guidance (for example by learning also loop arrangements and filters instead of only the waveforms) these neural networks can potentially create quite interesting music today (especially in the EDM/IMD genre). This development might be scary because it possibly replaces human creativity to a large extent, but you can't stop it by claiming that it's impossible or that it will always be of poor quality. People have said the same when synthesized music arrived, that it lacks human aspects etc., and now it's has a high cultural significance, even though it uses things like auto-tune and consists of super clean loops.
- crucialfelix 11y ago
  > for example by learning also loop and sample arrangements instead of only the waveforms This is what I mean - its not about the loop and sample arrangements. I've been doing exactly that since the 90s and I've made some great twisted computer generated stuff. Logical or predictable methods will always result in logical and predictable music. Its all about setting up code environments so that you can generate accidents and mutations and capture them. Its all about reacting and capturing it and putting it on wax (historically speaking). But that doesn't mean that a computer can understand why or even recommend what is amazing. We humans don't even know why some things are so great. A major thrill is finding some sound that is so twisted (ill, stoopid, sick) and totally bypasses the rational mind, shuts your thinking down and you get a big smile and start jumping up and down acting like an idiot. Then somebody else copies your track, then it becomes a style, then it becomes a cliche, then beatport is filled up with boring copie, then it becomes a sample set that people can buy, then somebody makes an app that can auto-generate that style and then they claim that computers are making music. But they are just playing it back, just like tall the human copycats further upstream. And the entire network of software, creators, audience and cultural is what we call music. > This development might be scary because it possibly replaces human creativity to a large extent, but you can't stop it by claiming that it's impossible or that it will always be poor quality. That's what I've spent a lot of my life engaged in. It often makes great music, but the machine cannot understand why. You'll need strong AI for that and it will need to have tensions, depressions, a body, chemical feelings, sex drive, longing and a strong psychological need to be lost in song. Then it could say "David, I think I've found a song you might like."
melloclello 11y ago
> Machines will never understand why humans don't really understand why either, if it didn't stop us, why should it stop a machine?

sweezyjeezy 11y ago

The image stuff works because we have found a way to model a good prior for it : convolution layers are basically enforcing some positional invariance and locality constraints on what our model believes the world looks like. Without this very strict prior, image recognition with neural networks just wouldn't really work. We haven't found a way to enforce a good prior for temporal data like sound yet.

TheOtherHobbes 11y ago
I realised a while back a lot of computer music is really computer science music - it's people who know something about computers but not much about the art of music, playing with relatively trivial algorithms to create music-like results. There's also the academic musical equivalent - music professors using stock faddy techniques like serialism or (currently) number and group theory. It's not that this is an impossible problem. It's more that the set of people who can code machine learning algorithms and understand music theory and are creative enough to invent new algorithmic techniques and to create more-than-listenable music is incredibly small - double figures, if that. So progress in non-trivial computer music has been incredibly slow. The DSP side has been far more successful, because DSP is - in most ways - a much simpler problem.
- crucialfelix 11y ago
  The reason for it has a lot to do with how Academia works. You need to write papers and produce innovative works. If you don't then you won't get funding or advance your career. > or (currently) number and group theory. That was Xenakis back in the 1950s ! Xenakis is the exception. He's really the foundation of academic computer music and his music is amazing and moving and his compositional concepts are still being hacked out by music programmers today.
- skwosh 11y ago
  I somewhat agree about computer music, though it appears to be an extension of the process driven composition that has been part of western (art) music for a while now (e.g. modulation). Sometimes I wonder if linguistics has more to offer composition than algebra (speculation, as I know next to nothing about (non-CS) linguistics). > It's not that this is an impossible problem. It's more that the set of people who can code machine learning algorithms and understand music theory and are creative enough to invent new algorithmic techniques and to create more-than-listenable music is incredibly small - double figures, if that. Are you pursuing something like this? Or know anyone who is? This is one of my main interests (alongside better interfaces for composition and well, making music). I'm actually back at University for my second degree to study this sort of thing. If you have a blog or anything I'd be interested to find out...
- ThomPete 11y ago
  Music is (dis)harmonies over rythmic patterns. There isn't anything inherently artistic about humans that computers can't replicate with time even the ability to compose an original song. That is besides the lives of humans and their appearance and history which is important but not the only factor. The irony is that musicians are actually striving for, but failing at, reaching the perfection level that computers have. And so for computers to sound more human like they have algorithms that make them more "sloppy" What composition algorithms lack is not the ability to compose like humans but a life that will give them angels and a story. Then again a lot of music is really formulaic anyway and computers are used for most of it. There is nothing in a few years that will hinder some sort of computer star to be born. But it's probably never going to connect with us the same way another human can. Not for now at least.
crucialfelix 11y ago
I think that music is not just the sound and not just the observable elements. It literally moves your emotional system around. We get shivers, we get horny, our mind shuts down and we get swept away in memories. That's not because of any specific arrangement of machine-observeable sound objects. > on what our model believes the world looks like Its not even doing that. Those images are just simple 2D fields of pixels with color data. It has no idea about any worlds. The spatial cues are way off, but we understand art (impressionism through messy expressionism, glitch) so we forgive it. And because they chose images with puppy eyes and we like puppies. Take away the animal elements and everybody would say the images were boring crap.
murbard2 11y ago
It helps but it's not necessary. Schmidhuber obtained state of the art performance on MNIST with a very vanilla deep net (no convolutional layer, no pooling, nothing fancy, just fully connected sigmoid units)
- sweezyjeezy 11y ago
  MNIST is really easy, it's not a useful benchmark any more.
  - murbard2 11y ago
    Sure, but the fact that you get state of the art result without a convolutional prior ought to at least support the argument that the prior is not necessary
    - sweezyjeezy 11y ago
      You can get something like 5% error rate on MNIST with a well tuned linear classifier, it's not comparable with ImageNet. Note that the computer vision techniques used before convnets used things like SIFT features, which are another way of imposing a (sort-of) prior. I do believe that some sort of strong prior is necessary for the problem.

aflinik 11y ago

I think I like your musical taste.

rndn 11y ago

> Its boring as hell. Machines will never understand why That statement seems to be overly strong. Perhaps we are far from having a machine that can figure out high level aspects of a song on its own, but I don't think it's implausible that with some more guidance (for example by learning also loop arrangements and filters instead of only the waveforms) these neural networks can potentially create quite interesting music today (especially in the EDM/IMD genre). This development might be scary because it possibly replaces human creativity to a large extent, but you can't stop it by claiming that it's impossible or that it will always be of poor quality. People have said the same when synthesized music arrived, that it lacks human aspects etc., and now it's has a high cultural significance, even though it uses things like auto-tune and consists of super clean loops.

crucialfelix 11y ago
> for example by learning also loop and sample arrangements instead of only the waveforms This is what I mean - its not about the loop and sample arrangements. I've been doing exactly that since the 90s and I've made some great twisted computer generated stuff. Logical or predictable methods will always result in logical and predictable music. Its all about setting up code environments so that you can generate accidents and mutations and capture them. Its all about reacting and capturing it and putting it on wax (historically speaking). But that doesn't mean that a computer can understand why or even recommend what is amazing. We humans don't even know why some things are so great. A major thrill is finding some sound that is so twisted (ill, stoopid, sick) and totally bypasses the rational mind, shuts your thinking down and you get a big smile and start jumping up and down acting like an idiot. Then somebody else copies your track, then it becomes a style, then it becomes a cliche, then beatport is filled up with boring copie, then it becomes a sample set that people can buy, then somebody makes an app that can auto-generate that style and then they claim that computers are making music. But they are just playing it back, just like tall the human copycats further upstream. And the entire network of software, creators, audience and cultural is what we call music. > This development might be scary because it possibly replaces human creativity to a large extent, but you can't stop it by claiming that it's impossible or that it will always be poor quality. That's what I've spent a lot of my life engaged in. It often makes great music, but the machine cannot understand why. You'll need strong AI for that and it will need to have tensions, depressions, a body, chemical feelings, sex drive, longing and a strong psychological need to be lost in song. Then it could say "David, I think I've found a song you might like."

melloclello 11y ago

> Machines will never understand why humans don't really understand why either, if it didn't stop us, why should it stop a machine?