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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 local

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.
  - BasDirks 11y ago
    "What composition algorithms lack is not the ability to compose like humans but a life that will give them angels and a story." Quote of the month for me. Bravo =]
  - TheOtherHobbes 11y ago
    I think that's a good example of what I'm saying - just because you don't understand the details doesn't mean professional musicians and composers don't have much deeper insight into music than you do. If you think music is [list of numbers] that can be made more "human" with a bit of timing randomisation, then of course it's all perfectly straightforward. In reality there's rather more happening. >What composition algorithms lack is not the ability to compose like humans but a life that will give them angels and a story. No, the music basically sucks as music. The number of people willing to listen to it voluntarily without being paid to - usually as students or academics - is vanishingly small. The story part only becomes relevant after that problem is solved. And while it's true that music is formulaic, it's also true that computer music hasn't yet worked out how to copy all the details of the formulas - never mind produce original and memorable new formulas from scratch. The best formula copier is probably Cope's EMI, and that sounds exactly like what it is - a slightly confused cut-and-paste cliche machine, not a human composer with a point to make.
- tudorw 11y ago
  http://www.cristianvogel.com/neverenginelabs/about http://www.cristianvogel.com/neverenginelabs/about
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.

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.
- BasDirks 11y ago
  "What composition algorithms lack is not the ability to compose like humans but a life that will give them angels and a story." Quote of the month for me. Bravo =]
- TheOtherHobbes 11y ago
  I think that's a good example of what I'm saying - just because you don't understand the details doesn't mean professional musicians and composers don't have much deeper insight into music than you do. If you think music is [list of numbers] that can be made more "human" with a bit of timing randomisation, then of course it's all perfectly straightforward. In reality there's rather more happening. >What composition algorithms lack is not the ability to compose like humans but a life that will give them angels and a story. No, the music basically sucks as music. The number of people willing to listen to it voluntarily without being paid to - usually as students or academics - is vanishingly small. The story part only becomes relevant after that problem is solved. And while it's true that music is formulaic, it's also true that computer music hasn't yet worked out how to copy all the details of the formulas - never mind produce original and memorable new formulas from scratch. The best formula copier is probably Cope's EMI, and that sounds exactly like what it is - a slightly confused cut-and-paste cliche machine, not a human composer with a point to make.
tudorw 11y ago
http://www.cristianvogel.com/neverenginelabs/about http://www.cristianvogel.com/neverenginelabs/about

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.