111–120 of 160 posts
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?
It is my understanding that capture based emulation of a given sound seems like a better idea than it really is. Say you capture a given amp or pedal sound - even if you get the latency down to acceptable #s, what if you want/need to turn a knob? Tweaking knobs is an essential part of the process of dialing in a sound relative to your guitar and output environment. Contrast with the Fractalaudio approach of modeling…
the problem with this approach is you need to have knowledge about the circuit to make it happen, not only that, but you also need people, who can reconstruct the circuit digitally this approach is not scalable (that's why the high cost) the ML approach doesn't require any knowledge about the system (black-box) to produce the result
It is my understanding that capture based emulation of a given sound seems like a better idea than it really is. Say you capture a given amp or pedal sound - even if you get the latency down to acceptable #s, what if you want/need to turn a knob? Tweaking knobs is an essential part of the process of dialing in a sound relative to your guitar and output environment. Contrast with the Fractalaudio approach of modeling…
The AxeFX is old news when it comes to guitar modeling. The newest Kemper firmwares do a much better job, and NeuralDSP's Quad Cortex does an even better job still along with the ability to capture distortion pedals. Modeling real gear is all fine and dandy, but what really has potential is being able to replicate circuits that aren't viable in the real world, like a tube amplifier on the edge of occilation or one ru…
Umm. You and I are just random people on the internet. I have spent a lot of time trolling the places pro musicians talk about the leading devices and I have found that Fractal is very widely considered to be the best of the best as far as sound quality goes. Kemper is known to be easier to use and Neural is the new kid on the block that has bluetooth, touch screen on device and footswitches that act as knobs. For the limited # of tones you get, it is supposed to sound great. None of those features are advantageous to me at all.
>being able to replicate circuits that aren't viable in the real world
That is definitely something that Fractal does. Considering the two devices you are talking up are based on capturing actual tones from real world devices, I am unsure of your point here.
>capture modulation effects
Modulation and time based effects have been modeled to perfection in the digital realm for a long time now. See the ubiquity of Strymon, etc. Fractal has equally good algorithms and allows incredibly intricate and signal paths as it is an all-in-one device. I have four expression pedals and 10 switches that can be programmed to control any parameter I would desire with a couple clicks of a mouse. No other multi effects device brings this degree of controllable complexity.
Earlier quoted context omitted.
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.
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…
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 constructor. Those two states represent the long term and short term memory features.
You would need to have an LSTM with hidden_size large enough to store the samples (or a compressed representation) of your entire loop. Not to mention you'd run into other issues with handling the logic around variable length loops based on a pedal toggle.
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 hand, I've been a bit dissatisfied with amplifier and cabinet models based on traditional DSP and physical modeling approaches, so maybe neural networks could fill in some of the gaps.
Honest question: if you're a musician, what is the appeal of digital modeling? Is it purely affordability/accessibility, or are you drawn to it because it would create different sonic possibilities that you couldn't get from the original?
That's my primary motivation. On my AxeFx, I have signal chains that are impossible with real gear. If I want to tweak that chain, it is a couple clicks of a mouse. I have four expression controllers and 10 foot switches that are tied to different parameters. I can tweak this functionality on the fly. All in a single $2k box.
Beyond all, tube amps are stupid for bedroom players as they generally require gig level volume to get the killer tonez...
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…
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. (Not necessarily this pedal, but I suspect ML will model the effects more than adequately for human hearing precision.)
FWIW, I say this as someone who used to argue that graphics programmers were doing gamedev all wrong because they weren’t modeling light, they were approximating light. ML models were the way out.
I also think much of the problem is that ML devs often don’t have traditional signal processing experience, so they haven’t been modeling signals in quite the right way. (I’m trying to rectify that a bit with my FFT tutorials: https://twitter.com/theshawwn/status/1398796224921321472?s=2...) It remains to be seen, but Fourier space has recently been making strides in ML, and it’s likely much easier for a model to approximate a nonlinear waveform in frequency space than as a raw waveform.
To put it another way, if human speech is getting to the point where ML models can trick people, what are the chances that a future model won’t be able to do it for guitars?
Can any skeptics weigh in? To me, this kind of tech is centuries away from fooling tone hounds.
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…
https://acris.aalto.fi/ws/portalfiles/portal/41964332/Real_t...
those are MUSHRA tests, meaning only skilled listeners are allowed to participate https://en.wikipedia.org/wiki/MUSHRA
TLDR: the resulting neural networks for given amplifier models were rated as “excellent” by the listeners, some even outperformed the reference!
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.…