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Neural networks emulate any guitar pedal for $120

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Re: Neural networks emulate any guitar pedal for $120

#11
post #9

Earlier 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 could.. but should you? I appreciate the ingenuity, but tuning this tech is time consuming. I prefer to just carrying on playing my guitar poorly.

you can download models online, no need to train yourself, which i agree is time consuming

Re: Neural networks emulate any guitar pedal for $120

#12

The video won’t play for me, but does this allow tweaking of parameters? The problem with most projects like this one is that it replicates the sound of a certain pedal with certain settings. That’s a major problem for most guitarists as playing with the knobs live is a big part of the attraction to pedals.

No, it doesn't. But to be honest, this would be next level. When looking at this, I'm thinking of alternatives and I can only name 2:

1 - Kemper Profiler

2 - NeuralDSP

Both of them are above 1000€/$. We are talking about 10x the price of this thing. Add some Multi-FX Pedal (like Line6 HX-Stomp) where you put this in the FX-Loop and you end up with something equally good for still half the price.

And in general, its not about what is better, digital or analog. It's about the use-case. In the studio or when noodling around with the knobs when practicing: Real Amps and Pedals. But on stage, you don't play with the settings of your pedals or you want presets. This is where you go digital. No one will notice the slightly different sound there anyway.

Re: Neural networks emulate any guitar pedal for $120

#13
post #7

any 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

Re: Neural networks emulate any guitar pedal for $120

#14
As 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.

Re: Neural networks emulate any guitar pedal for $120

#16

The video won’t play for me, but does this allow tweaking of parameters? The problem with most projects like this one is that it replicates the sound of a certain pedal with certain settings. That’s a major problem for most guitarists as playing with the knobs live is a big part of the attraction to pedals.

No, it doesn't. But to be honest, this would be next level. When looking at this, I'm thinking of alternatives and I can only name 2: 1 - Kemper Profiler 2 - NeuralDSP Both of them are above 1000€/$. We are talking about 10x the price of this thing. Add some Multi-FX Pedal (like Line6 HX-Stomp) where you put this in the FX-Loop and you end up with something equally good for still half the price. And in general, its n…

I played out live for a decade and absolutely used the knobs on my pedals live. Most musicians I knew with a pedalboard did the same.

That said, if you use a preset based effects setup like rackmount gear, I could see how this would be cool.

Re: Neural networks emulate any guitar pedal for $120

#17
post #9
post #7

any 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.

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 - distortion effects in particular deal with enormous amounts of distortion (> 100% THD) which creates spectrums far outside the range of hearing. Digital audio systems need to have high orders of oversampling to prevent audible aliasing (8-16x is not unheard of!).

After aliasing is memory. It's too early in the morning for me to do math but I'm almost certain you can't model a looper with a causal NN that has less internal state memory than the length of your loop. Doing so is dumb anyway, since loopers are pretty trivial and their biggest cost is memory. Same goes for digital delay and modulation effects, the algorithms are not expensive.

Re: Neural networks emulate any guitar pedal for $120

#18

I'm guessing a Hi-Z input before this contraption could improve the tone and do the pickups of the guitar or bass justice. A dedicated buffer pedal or just a pedal with buffer bypass perhaps?

Yes I was surprised to see the guitar signal going in directly. A simple DI box would improve the sound noticeably.

Re: Neural networks emulate any guitar pedal for $120

#19
The cool bit about this to me is that $120 in hardware is a the one-off prototyping cost. As a product it could be made much cheaper.

Another benefit to a software defined pedal is that it can express sounds that cannot be replicated in analog. Emulation is boring. Train it to do stuff that I can't buy in a pedal!

Re: Neural networks emulate any guitar pedal for $120

#20
post #7

any 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.

If you want to be pedantic it probably can't emulate a guitar pedal that you can tap a representation of a Turing machine into and it will only make a sound if the machine halts.
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