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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

#141

Post by jeeceebees »

lunixbochs wrote: post #114
jeeceebees wrote: post #83

Earlier quoted context omitted.

LSTM stands for Long Short Term Memory. It's a recurrent network that learns what and how long things should be kept in its internal state buffer. It doesn't have a fixed state size because it's just learning a nonlinear function that takes an input and a state to an output and a new state. Obviously it can't model all possible, infinite length recurrences, but it can definitely do a pretty good job of approximating…

I don't think that assessment is quite right. The hidden size is fixed - the second argument to Pytorch's nn.LSTM constructor is "hidden_size – The number of features in the hidden state h". A call to `y, hidden = layer.forward(x)` (where x has a batch size of 1, and an arbitrary length) produces two hidden states of dimensions `(1, 1, hidden_size)`, where hidden_size is the exact number you passed to the LSTM constr…

The hidden state isn't storing the samples of your loop (or a compressed version of your loop). It's encoding a representation of how the output will change based on what the current state and input are. This might be strongly dependent on what the exact samples in the loop are, but it could also be more general. I think it's missing a bit of the representational power of an LSTM to see the state representation as just a buffer of the current input.

But, yeah, at some point your signal has such a complex behavior on long time scales that there isn't a good way to predict it based on a limited state size (or at least gradient descent can't find a function to predict it for you).

Re: Neural networks emulate any guitar pedal for $120

#142

Post by [deleted] »

jeeceebees wrote: post #141
lunixbochs wrote: post #114

Earlier quoted context omitted.

I don't think that assessment is quite right. The hidden size is fixed - the second argument to Pytorch's nn.LSTM constructor is "hidden_size – The number of features in the hidden state h". A call to `y, hidden = layer.forward(x)` (where x has a batch size of 1, and an arbitrary length) produces two hidden states of dimensions `(1, 1, hidden_size)`, where hidden_size is the exact number you passed to the LSTM constr…

The hidden state isn't storing the samples of your loop (or a compressed version of your loop). It's encoding a representation of how the output will change based on what the current state and input are. This might be strongly dependent on what the exact samples in the loop are, but it could also be more general. I think it's missing a bit of the representational power of an LSTM to see the state representation as ju…

[deleted]

Re: Neural networks emulate any guitar pedal for $120

#143

Post by bozzcl »

jimnotgym wrote: post #132

In my attic there is a dusty box with a multi effects unit from the 90's. The reason I stopped using it is that it is not that the fuzz doesn't react to the guitars volume pot like a Fuzz Face or because the Tubescreamer was a TS9 not an 808. It was because having a single box with all of your effects in is a faff to tinker with. I like the dedicated hardware of my pedals, I like having the right number of knobs. I l…

You can do all that with modern multi-effects, and even more.

I'm running a hybrid setup, using a Line 6 Helix in front of a Soldano SLO 30 (and on its effects loop as well). Each effect can be adjusted (with all sorts of knobs, more than 12 for some), moved around and triggered individually or together. You can even use the Helix to toggle channels in the amp, manually or as part of presets.

It drastically eased up my workflow to experiment with and mix effects. No more tinkering with cables. No more wondering which pedal is plugged in wrong or doesn't have enough power. No more velcro strips. Plus, selling my hoard of pedals felt nice.

Re: Neural networks emulate any guitar pedal for $120

#144

Post by mkl »

ratww wrote: post #106
PaulDavisThe1st wrote: post #89

Earlier quoted context omitted.

polyphonic note detection is largely solved at this point. But "solved" here means "when not doing the analysis in real time". The realtime solutions are not as good. NN's are not typically great at realtime either, so this may not help very much with this particular goal.

The video in TFA is in real time, so I don't believe your assumption is correct.

I don't think TFA involves polyphonic note detection, just effects processing of the whole audio signal.

Re: Neural networks emulate any guitar pedal for $120

#145

Post by ratww »

mkl wrote: post #144
ratww wrote: post #106

Earlier quoted context omitted.

The video in TFA is in real time, so I don't believe your assumption is correct.

I don't think TFA involves polyphonic note detection, just effects processing of the whole audio signal.

I never claimed or implied anywhere that it does, and I know for a fact TFA doesn't involve note detection.

I'm just disputing the GP general assertion about NNs that "NN's are not typically great at realtime either", which is quickly disproven by the TFA which uses NN for realtime audio.

Re: Neural networks emulate any guitar pedal for $120

#146

Post by nyanpasu64 »

sillysaurusx wrote: post #138
nyanpasu64 wrote: post #137

Earlier quoted context omitted.

I've heard images are better modeled in DCT space (which isn't based on complex numbers) because it's better at energy compaction than FFT, and also because it doesn't assume that the image is periodic. Also some people think that the FFT is insufficient, even for audio, because it doesn't model time-domain hearing perception. Some people say that wavelets are better at modeling images than purely frequency-domain tr…

All excellent points, and I think you should DM me on twitter to chat about this more. (I hope you will!) DCT is on my radar. But there are several serious limitations that I think are overlooked. For example, convolution is no longer a simple component-wise multiplication. This seems, to me, a big deal. Complex numbers are tricky to model, but I think most people have given up too easily, or haven't been creative en…

Sorry I don't work on neural networks much, and have my plate too full with other projects (and my DSP is a bit rusty?) to hold a conversation on this right now. And I don't use Twitter much either.

Maybe we can talk later? Not sure.

Re: Neural networks emulate any guitar pedal for $120

#147

Post by sillysaurusx »

nyanpasu64 wrote: post #146
sillysaurusx wrote: post #138

Earlier quoted context omitted.

All excellent points, and I think you should DM me on twitter to chat about this more. (I hope you will!) DCT is on my radar. But there are several serious limitations that I think are overlooked. For example, convolution is no longer a simple component-wise multiplication. This seems, to me, a big deal. Complex numbers are tricky to model, but I think most people have given up too easily, or haven't been creative en…

Sorry I don't work on neural networks much, and have my plate too full with other projects (and my DSP is a bit rusty?) to hold a conversation on this right now. And I don't use Twitter much either. Maybe we can talk later? Not sure.

No worries :) it was just an offer. It surprised me how much you knew about the domain. Good luck with your projects!

Re: Neural networks emulate any guitar pedal for $120

#148

Post by vict00ms »

ushakov wrote: post #73
riku_iki wrote: post #69

Earlier quoted context omitted.

> this approach is not scalable (that's why the high cost) It may be opposite, most of the amps follow some classic schematic (e.g. jtm, plexi, princeton) with insignificant changes, so after building digital copies of some limited number of classical amps they can add new one rather fast. As result, fractal has about 100 high quality models already (average guitarist probably uses 5?). > the ML approach doesn't requ…

sure, but can it model my amp? i doubt they will ever add it! correct, ml requires data, but you don't need to capture every possible position to do a good prediction only a handful would suffice and let's be honest, how many presets do you really need (average guitarist probably uses 5?)

>sure, but can it model my amp?

I believe you can already create your own impulse responses (from your personal amps).

Re: Neural networks emulate any guitar pedal for $120

#149

Post by u_gene »

Yo People!! A friend keeps sending links to guitar pedal discussions to me and I thought I chip in to the conversation.

While you can emulate any guitar pedal with AI and you don't have to pay 120$ I'd like to introduce you to some understanding.

MUSICIANS love to purchase shit they can show off to others. it's in their ego nature. While 120$ for software might compete with quad pro cortex pedal, you can't show off with 120$ but with a pedal you bought fo 2g you can. That is why BOSS made a big travel box for physical pedals this year, this is what they released at the NAMM2021.

How do I know that? I make software for them. GarageBand has 1000s of different pedals already and those are free. most musicians have apple or abelton live or some similar.

Also, not to discourage anyone - live music is dying. More and more you see musicians dancing on the stage with their instruments pretending they are playing, while pro-edited backtrack is going through the speakers.

If you want to make this world a better place think how to improve housing/farming/healthcare with technology. Things like roof above the head, food on the table will be always in demand. There is no money to be maid with musicians, cause they don't have any. it took me few years to realize, you can learn on my mistakes.

If you have some cash and time and thinking if you need to build some software product - kill that, buy some land and build a cabin. In the end you'll have another property. you can sell it or rent it. you can live it to your kids, there is value there. There is no value in your software idea unless people use it like crazy, and they really won't cause they already have 100 pedals and that new pedal bag from boss

Re: Neural networks emulate any guitar pedal for $120

#150

Post by u_gene »

patfla wrote: post #122

I stopped reading at the first sentence: "It’s a well-established fact that a guitarist’s acumen can be accurately gauged by the size of their pedal board- the more stompboxes, the better the player." As both a software engineer and a guitarist, I'd say the opposite is true. Or at least truer. You can't do math-rock without a lot of pedals but the hard part is to acquire the chops. A lot of pedals, and production eff…

..but most pedal users buy the pedal to.. show off in-front of others who don't have that pedal. period.

GarageBand has 1000 of pedals, people don't need more pedals, they need to practice more, but they don't understand that. That's why they are buying more pedals while keep dragging about how much they care about green/clean planet.

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