3 ms·

I agree with most things you say, but "you can't get a bad sound out of it" is pretty subjective, especially when it comes to synthesis. So how would one define

I agree with most things you say, but "you can't get a bad sound out of it" is pretty subjective, especially when it comes to synthesis. So how would one define the training set of "good sounds" for supervised training? One thing I thought would be pretty cool - I've got a friend who did a phd on physical modeling of sound in Edinburgh - http://www.ness.music.ed.ac.uk/ http://www.ness.music.ed.ac.uk/. Often those physical models have hundreds of parameters and it's quite difficult to tune them to make meaningful/good sounds. Perhaps a neural network would be useful for tuning the parameters - you could use a real musician playing a real instrument with sensors, and use the generated sensor data and audio for your training set. And then the neural network could learn to match the physical model parameters to that.

posterboy 9y ago
You should check the other projects of magenta, they did something like you describe with piano using 1400 training pieces. Now they only need user feedback to find out which outputs of the network are garbage ... oh wait, maybe they are already doing that, who knows. Well, they are probably not tuning 100 instrument parameters but only the play characteristics like accent, velocity and so on.
anigbrowl 9y ago
It's absolutely subjective, eg as a simple example I love the 303 and you may hate it but adore some other model. But I generally understand a comment like to mean both that the person likes the timbrality of a particular instrument, and that one won't 'lose' the sound of a patch while tweaking. I'm not sure how to articulate this. I have synths that I can program in my head because I know the architecture really well and when I imagine a sound I can walk over and dial it in from the front panel and get more or less what I expected, plus I can then tweak the results with abandon and for musical satisfaction. Then I have others (sometimes from the same manufacturer which emit all manner of nice sounds but are far harder to program and easily veer off into sonic mush - technically impressive but not really fun to play.
- dharma1 9y ago
  Yeah I know what you mean. The "sweet spot" on some synths. Just don't know how to pose that as a supervised learning problem, or what it would do (limit the range of parameters?) I'm not sure the amount of parameters in normal substractive synth+filter+mod architecture is large enough for very interesting results that you couldn't achieve otherwise, or how you would generate a training set that can produce meaningful mapping. I also think wavenet sample by sample generation and interpolation between latent features doesn't sound that exciting, as cool as it technically is. We'll find some place to use machine learning in music/audio eventually :) I think perhaps more natural sounding pitch shifting could be one area (since you could learn the structure of sound of different instruments at various pitches), reverb removal, denoising, polyphonic audio to midi - things like that, where you have obvious training data.

posterboy 9y ago

You should check the other projects of magenta, they did something like you describe with piano using 1400 training pieces. Now they only need user feedback to find out which outputs of the network are garbage ... oh wait, maybe they are already doing that, who knows. Well, they are probably not tuning 100 instrument parameters but only the play characteristics like accent, velocity and so on.

anigbrowl 9y ago

It's absolutely subjective, eg as a simple example I love the 303 and you may hate it but adore some other model. But I generally understand a comment like to mean both that the person likes the timbrality of a particular instrument, and that one won't 'lose' the sound of a patch while tweaking. I'm not sure how to articulate this. I have synths that I can program in my head because I know the architecture really well and when I imagine a sound I can walk over and dial it in from the front panel and get more or less what I expected, plus I can then tweak the results with abandon and for musical satisfaction. Then I have others (sometimes from the same manufacturer which emit all manner of nice sounds but are far harder to program and easily veer off into sonic mush - technically impressive but not really fun to play.

dharma1 9y ago
Yeah I know what you mean. The "sweet spot" on some synths. Just don't know how to pose that as a supervised learning problem, or what it would do (limit the range of parameters?) I'm not sure the amount of parameters in normal substractive synth+filter+mod architecture is large enough for very interesting results that you couldn't achieve otherwise, or how you would generate a training set that can produce meaningful mapping. I also think wavenet sample by sample generation and interpolation between latent features doesn't sound that exciting, as cool as it technically is. We'll find some place to use machine learning in music/audio eventually :) I think perhaps more natural sounding pitch shifting could be one area (since you could learn the structure of sound of different instruments at various pitches), reverb removal, denoising, polyphonic audio to midi - things like that, where you have obvious training data.