Neural networks emulate any guitar pedal for $120

Not much need to (or benefit in doing so). If you're somebody like AnalogMan, building pedals with careful selection of NOS transistors wired point-to-point, this is so utterly not a threat to you.

I've seen entirely analog circuits designed and built with the PCB parts and techniques used in these devices, that are miles away from the quality of good pedals, without even being digital or modeling at all. I'm sure the problem circuits measure completely fine, and then you A/B them with a truly great pedal and it's chalk and cheese.

If you try to argue the point, people committed to the digital modeling model get quite fierce, so tone hounds learn to just roll their eyes and not engage. You can also think of it as learning to rely on secret weapons.

The time in the RNN might be the only thing that's hard. We can commonly do A/D, D/A pretty quick. I can make a db request over a network, have it parse the SQL, execute it reading a bunch of SSD pages and return the sorted results in about 1ms.

The answer would probably be to reduce the 'learned' output to be a convolution kernel that gets run rather than the RNN itself on the input. Then the kernel only has to change gradually to produce a different sound not continuous processing to produce a particular sound.

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 each component of a device. Fractal's AxeFx is the gold standard and any geek would gush over the HW and SW engineering. The best part is that the company owner keeps improving his algos and pushing out updates for free. This device costs the equivalent of a good amp head and is loaded with more amps and effects than any of the competition.

Sorry if this sounded like an ad, but I am always surprised how little airtime this amazing product gets in hacker circles.

I designed and built a eurorack module around a pi zero with a usb audio input/output, voltage divider, op amp, and some pots connected to adc. Using rtaudio the latency was pretty low and gave me control of the parameters I wanted.

Given that this was the only really active process on the pi, it ended up working really well. I simply converted modules I had written for vcv rack.

I also built one that enabled usb host mode and acted as an audio device that worked with any daw. Ended up being pretty cool for about $20 of parts.

While not as cool as an ml system, given that I was already writing dsp, it ended up being pretty neat.

> 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.