151–160 of 160 posts
Can any skeptics weigh in? To me, this kind of tech is centuries away from fooling tone hounds.
Tone hounds do a good enough job of fooling themselves.
For analog pedals, I'd expect better results from circuit modeling. Given the wide (and very non-linear) range of settings of a typical pedal, as well as interaction (impedance, etc.) with a real guitar and amplifier, it seems like it would be a pain to get all of the training data. For a digital pedal, running the actual (e.g. Eventide) DSP code is just going to be better than some ML approximation. On the other han…
ML dilettante here! I wouldn’t discount ML. The nonlinearities are the bread and butter of modern ML models. In fact, two linear layers without a nonlinearity inbetween is equivalent to one big linear layer. So nonlinearities are required. To put it another way, I would gladly bet any reasonable sum of money that in a double blind test, the listener wouldn’t be able to tell the difference from a genuine guitar pedal.…
I guess I didn't get my point across. What I meant was that pedal settings tend to be non-linear with multiple sweet spots (which often depend on the guitar and amp) so you shouldn't just do a linear range from 1-10^N (where N is the number of knobs) for training data, as someone else had suggested. Moreover, there are also dependencies on the impedance chain, gain structure, feedback, reflections, etc., which seem well-suited to circuit and physical modeling. Digital pedals, as I note, are largely software anyway so it doesn't make sense to me to try to model them with ML any more than it does to model Microsoft Word using ML (though I'm sure someone has tried.)
In general ML seems most useful when you don't have good analytical models - but in the case of circuits and software we have very good analytical models.
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ML dilettante here! I wouldn’t discount ML. The nonlinearities are the bread and butter of modern ML models. In fact, two linear layers without a nonlinearity inbetween is equivalent to one big linear layer. So nonlinearities are required. To put it another way, I would gladly bet any reasonable sum of money that in a double blind test, the listener wouldn’t be able to tell the difference from a genuine guitar pedal.…
> The nonlinearities are the bread and butter of modern ML models I guess I didn't get my point across. What I meant was that pedal settings tend to be non-linear with multiple sweet spots (which often depend on the guitar and amp) so you shouldn't just do a linear range from 1-10^N (where N is the number of knobs) for training data, as someone else had suggested. Moreover, there are also dependencies on the impedanc…
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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…
If the future input samples have a meaningful impact during loop playback, then it hasn't learned the correct behavior of the original loop pedal.
Note that the linked project appears to use a hidden size of 20. Twenty floats. With that much space we're very much back to "sure, you might theoretically be able to loop if the information fits in the hidden size".
Increasing the hidden size beyond 20 still won't solve learning the complex state machine behavior of an original loop pedal, which can loop variable length audio. You'd need to provide the pedal state to the network in addition to the audio, and probably train need to train it on a bunch of different loop lengths (>thousands?).
This would mostly be an academic pursuit, as it's extremely impractical compared to the other uses of the device.
…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.
There is no reason why this would run at “MP3 quality”, given that it would be a really bad idea to compress the audio data before running it through an algorithm. I would expect it’s at minimum CD quality, and perhaps better, depending on the fidelity of the A/D and D/A stages and the bandwidth of the algorithm.
I want to know if anyone ever uses this to create a fleet of classic microphone sounds from a working mic like the SM57. Sennheiser, Neumann, Telefunken, AKG. Hell, you could stack it with preamp filters like Neve. It would bring me great pleasure to see a studio chock full of SM57s masquerading as the best microphones money can buy.
This 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.
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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.
I know some who change their settings during playing, but I wouldn't consider this as standard. Also, you can still add a real Pedal to a MultiFX Board if you really want to change its settings live. On the contrary, automatically changing the settings controlled by your DAW and triggered via MIDI into your MultiFX unit is also pretty common. You don't have to hassle with your Effects at all and can concentrate on playing, what you should, as you need to be absolutely on point.
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Tone hounds do a good enough job of fooling themselves.
I'd put money (a few thousand USD) up against anyone who can fool me that I'm playing on my rig when I'm not. It's a very simple rig. Because that'd be valuable if I ever lost and I'd collect up until I did.