4 ms·

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 t

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

riku_iki 5y ago
> 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 require any knowledge ML approach requires you to capture training data: which is different sound samples with all possible knobs positions (8 knobs per amp in average) + different types of speakers and mics, which is very huge number of variations. > that's why the high cost cost is driven by market, ML profiling competitors (kemper, neuraldsp) charge about the same for their devices.
- ushakov 5y ago
  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?)
  - riku_iki 5y ago
    > sure, but can it model my amp? i doubt they will ever add it! This is very different and more narrow use-case. Also they have tone match like forever, you build signal chain close to your amp, then add tone match block, which applies ML to voice your digital signal chain close to recording. Here is example: https://www.youtube.com/watch?v=hZnZ1nJODLo https://www.youtube.com/watch?v=hZnZ1nJODLo Also in this example he didn't profile actual amp, but actual AC/DC recording, and result is very good I think. > how many presets do you really need (average guitarist probably uses 5?) But how you find this preset for your signal chain (guitar + speakers)? That's one of big points of frustration with kemper: one needs to go through hundreds profiles (not necessary good quality) to find one which will sound good with his signal chain. With fractal: you take some basic preset, and change knobs to your tastes and goal as with real amp.
  - vict00ms 5y ago
    >sure, but can it model my amp? I believe you can already create your own impulse responses (from your personal amps).
wintermutestwin 5y ago
This is exactly what Fractal has achieved. They took actual amps and modeled the actual circuits. Basically, this work was done for a massive number of amps by one dude. And he sells this device for $2k - which is ~ the cost of a single good tube amp head.

riku_iki 5y ago

> 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 require any knowledge ML approach requires you to capture training data: which is different sound samples with all possible knobs positions (8 knobs per amp in average) + different types of speakers and mics, which is very huge number of variations. > that's why the high cost cost is driven by market, ML profiling competitors (kemper, neuraldsp) charge about the same for their devices.

ushakov 5y ago
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?)
- riku_iki 5y ago
  > sure, but can it model my amp? i doubt they will ever add it! This is very different and more narrow use-case. Also they have tone match like forever, you build signal chain close to your amp, then add tone match block, which applies ML to voice your digital signal chain close to recording. Here is example: https://www.youtube.com/watch?v=hZnZ1nJODLo https://www.youtube.com/watch?v=hZnZ1nJODLo Also in this example he didn't profile actual amp, but actual AC/DC recording, and result is very good I think. > how many presets do you really need (average guitarist probably uses 5?) But how you find this preset for your signal chain (guitar + speakers)? That's one of big points of frustration with kemper: one needs to go through hundreds profiles (not necessary good quality) to find one which will sound good with his signal chain. With fractal: you take some basic preset, and change knobs to your tastes and goal as with real amp.
- vict00ms 5y ago
  >sure, but can it model my amp? I believe you can already create your own impulse responses (from your personal amps).

wintermutestwin 5y ago

This is exactly what Fractal has achieved. They took actual amps and modeled the actual circuits. Basically, this work was done for a massive number of amps by one dude. And he sells this device for $2k - which is ~ the cost of a single good tube amp head.