There's no link to the vst. It would be neat if it was something you could just drop into your DAW and try to train it in wacky effects chains. The artifacts in it's sound might be really neat.
Neural networks emulate any guitar pedal for $120
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Re: Neural networks emulate any guitar pedal for $120
#22As much as I'm torn with my feelings about nVidia as a company, especially recently with their attempts to artificially limit hardware you own, I must admit Jetson family is incredibly capable and very well executed. I've been using Jetsons as RasPi replacements wherever I can, they are not only more capable but also much more reliable than RPI4s, in my not so limited experience.
Extremely expensive. Or overpriced?
Re: Neural networks emulate any guitar pedal for $120
#23…at MP3 quality.
This is fine if you like that sort of thing. I would note that latency is very important here: it's not going to be nice to play through if it's incurring any significant latency.
Re: Neural networks emulate any guitar pedal for $120
#24Earlier quoted context omitted.
you could model amplifiers, distortion effects, phasers and flangers, according to CoreAudioML project, which makes this possible https://github.com/Alec-Wright/CoreAudioML
You can model most useful nonlinear functions with neural networks, this is unsurprising. You can also use Volterra series. You can even estimate/measure the Volterra kernel then train a NN to model it instead of dealing with the computational complexity of generalized convolution for nonlinear dynamic systems. The hard part is that there are some fundamental limitations to deal with. The biggest is aliasing - distor…
Now I wonder if a NN would be able to learn a nonlinear effect without aliasing, even if run at the original sample rate. Oversampling and filtering are, after all, things that could become part of the model too. Perhaps it can learn to approximate them with less CPU cost than doing it for real.
Re: Neural networks emulate any guitar pedal for $120
#25any pedal? That seems far-fetched - one trivial counter-example would be a looper as that requires modal input and state. I also wonder if it can handle complex multi-tap delays. Can anyone give a rough idea of the actual limitations? I would guess that there is a limit to how non-local the effects it can manage are.
They claim to be using a LSTM, and I believe that any RNN-like architecture should (in theory) be able to learn a loop pedal. If you aren't familiar with RNNs, think about it like a NN that instead of learning a input -> output function, learns a (input, state) -> (output, newState) function
Only if the entire loop audio fits into the RNN's hidden state? Or if you use some kind of external memory mechanism that gives the network access to previous audio.
Re: Neural networks emulate any guitar pedal for $120
#26There's no link to the vst. It would be neat if it was something you could just drop into your DAW and try to train it in wacky effects chains. The artifacts in it's sound might be really neat.
check out https://github.com/GuitarML
Re: Neural networks emulate any guitar pedal for $120
#27Uli Behringer in a box ;)
Re: Neural networks emulate any guitar pedal for $120
#28have i mentioned yet that training only takes ~30 minutes and you only need ~3 minutes of data?
Re: Neural networks emulate any guitar pedal for $120
#29This is really cool. I wonder if this can be used to simulate pianos, rhodes and wurlitzer sounds.
I wonder too, but I think the result will not be better than the samples from which you train it.
Now that you mentioned samples: I wonder if neural networks will be able to help with polyphonic note detection, so we can trigger MIDI samples using an off-the-shelf guitar or other instruments.
There have been a few recent advancements lately (Boss SY-1), but even the supposedly "ideal" solutions, that require a new polyphonic pickup, are not good at all. I have a Fishman Triple Play and a plugin whose name I forgot, and tracking is frankly terrible.
Re: Neural networks emulate any guitar pedal for $120
#30[deleted]