131–140 of 160 posts
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…
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 like being able to turn off the fuzz but keep the delay by stepping on the fuzz switch. I like being able to run the TS before or after the fuzz and to see it happen. I mentally am much more at home with little boxes for each stage, it is like a real world flow chart!
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.
Just a quick note, you could solder together most classic guitar pedals for $20. You could buy reissues of many of them for less than $100. Big Muff, Proco Rat, Klon Centaur (Klones), Tubescreamer, Dynacomp, etc are all available for less than that. Just being clear that this is not a price issue.
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…
Have you tried the Fractal stuff? I've been using it since the first generation (consistently for live use since the Axe-Fx II days) and they've been ahead of the competition since their inception. At this point I'd venture to say that the majority of their amp models sound indistinguishable from the real thing with no advanced parameter tweaking.
That being said when I was between Fractal units about a year ago I spent a brief amount of time with the Pod Go and was immensely impressed with how much they were able to pack into a $500 floor unit. Most of the amps and drives still felt a bit caricature-y but were still very usable - a far cry from the Pod Bean days. It's truly a great time to be a musician.
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.…
Earlier quoted context omitted.
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'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…
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 enough in how they're modeling them. Some of my (outdated) ideas: https://gist.github.com/shawwn/c6865fccafac5066e1c7bab672781...
In other words, you're probably right, but I'm focused solely on FFTs on the (very low) chance that people have overlooked something that will work well.
Emulate 20 transistors using hundreds of millions of transistors!
Earlier quoted context omitted.
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.
Oversampling requires producing more output information than input information. It would be incredible for a NN to realize a system that could do this without requiring more memory and CPU cycles than a good oversampling algorithm, which can be derived analytically with various definitions of "optimal." A 3rd or 4th order polynomial interpolator is pretty darn good and doesn't need a NN to find the coefficients.
Oversampled DSP algorithms work by oversampling, performing the nonlinear processing, then filtering to remove the higher harmonics, and finally downsampling again. We do it this way because it's convenient and easy to understand and based on proven mathematics. But nothing says these steps have to be distinct.
An oversampled DSP algorithm looks like a regular DSP algorithm from the outside, perhaps with some more state and latency required for it to perform the internal oversampling. You can also imolement such an oversampled algorithm entirely at the original sample rate clock; it just means the processing needs to internally process several samples per outer loop sample.
Since neural networks excel at modeling "black boxes" as one amorphous blob that we don't understand, I wonder if a NN could learn to model such an internally oversampled algorithm fairly accurately, and what the computational complexity would be.
Since you can model the oversampling/filtering/etc steps as linear convolutions with wider internal state at the original sample rate, I'm almost certain this will work with the right NN topology. It's obvious an NN can implement oversampling.
And so my question is: could treating the combined oversampled processing as one step, and training a NN on that, potentially result in a more efficient implementation than doing it naively? Especially for heavy distortion that needs high oversampling ratios.