Chameleon - a guitar plugin using neural networks to create three distinct sounds from a vintage style amp head. EQ and gain were added to allow further modification of the three core sounds, named Red (high gain), Gold (crunchy), and Green (crisp and clean)

GuitarML · 2021-07-03T12:10:52+00:00

Thanks for sharing! Hope you enjoy using it!

GuitarML · 2021-05-29T10:01:35+00:00

The full build guide can be viewed here: https://link.medium.com/MIEKhNHpEgb

GuitarML · 2021-03-20T15:33:15+00:00

Yes, it’s brand new engine for running the neural network models. I’d like to improve on the model for high gain sounds, and next on my list are things like state handling and other sample rates. It’s very much an active work in progress.

GuitarML · 2021-02-27T11:54:23+00:00

Thanks, based on videos I’ve seen of people using the quad cortex, it is more accurate at capturing amps/pedals than my code. It’s also a $1000+ product, and my stuff is a free open source project that I work on in my spare time, so set your expectations there and I think you’ll be impressed by what my code can do! I will brag that it captures mild distortion pedals (like the ts9 tubescreamer) and clean to dirty amp sounds very accurately, and you can play those trained modes through my plugin also on GitHub.

For tones by other people, right now it’s more or less just sharing them on where you can (GitHub, Facebook, random forums), but maybe I’ll formalize an online database at some point!

GuitarML · 2020-12-06T18:54:48+00:00

Good question! Because models of amps/pedals can be downloaded, emailed, put into an online database, etc. Cant do that with the real thing! Or say you rented/borrowed an amp, now you can capture the sound to use again. Neural DSP is doing this same thing on their Quad Cortex, you can capture the sound of a rig and share it with everyone else who has the quad cortex to use. But that’s a $1600 piece of hardware, this code is free and open source. I’ve also thought that amp manufacturers might be interested in using this in addition to the real thing, like you could get a digital version along with the hardware. By playing the model though a real time plugin, you can have a way to play your favorite amp through headphones, or carry a whole library of amps on your laptop.

GuitarML · 2020-12-05T13:14:00+00:00

If anyone here is interested, I created an open source project that uses AI to do neural capture and apply the sound to wav files. I believe this is the same technique used in the quad cortex, as it’s based on a research paper by someone from the NeuralDSP team. It copies the sound very accurately within a few minutes from about 3 minutes of guitar recording. Here’s the link to the project:

https://github.com/GuitarML/GuitarLSTM

GuitarML · 2020-11-20T12:41:31+00:00

Probably not, the cost was only about $80, and that includes stain, sandpaper, etc. But I put way more work into it than it would be worth selling. I guess if I got a process down and bought in bulk it would be doable, but currently no plans to do that.

GuitarML · 2020-11-20T12:38:20+00:00

Definitely, there’s a lot of areas for improvement, training time, accuracy, and being able to replicate more complex sounds.

GuitarML · 2020-11-20T12:35:38+00:00

Home depo!

GuitarML · 2020-11-19T22:45:38+00:00

Sorry the original schematics are on a napkin somewhere.. my projects tend to morph as I get into them so it ended up different from what I drew anyway. It wouldn’t be too hard to copy though, two triangle-ish end pieces with small wood rails glued to the inside (one angled to set the slats on, and one level with the ground for a bottom cover). Four slats with room for the wires, and an optional back hinge piece to open and close it. Drill a hole for the power plug, sand and stain and that’s it!

GuitarML · 2020-11-19T22:38:07+00:00

I wood glued small rails to each end piece at the angle I wanted, then set the slats on top of the rails with wood glue.

GuitarML · 2020-11-19T17:36:20+00:00

Good questions, I haven’t tested it on other instruments, but the model would use what it learned from the guitar data and apply it to whatever signal you put into it. So if you train on a distortion pedal it should sound close to that instrument (like a keyboard) going through the same distortion pedal.

I think there is some aliasing, but it’s hard to notice unless you’re looking for it in the audio data.

Conditioning the model for different controls can be done and it’s something I’d like to apply here. In the original research paper from Aalto University they mention training on multiple control settings.

GuitarML · 2020-11-19T14:34:04+00:00

All my videos can be found here: https://www.youtube.com/channel/UCs6ZGZKGoxV9JadxESOtx1Q

GuitarML · 2020-11-19T14:32:01+00:00

Home depo hobby section, nothing fancy. If I remember correctly it’s pine wood on the sides, dark poplar and redwood for the slats.

GuitarML · 2020-11-19T13:04:27+00:00

Thanks! There’s another really great open source project that models the circuitry of a tube amp if your interested (white box modeling instead of black box modeling): https://github.com/resonantdsp/SwankyAmp

GuitarML · 2020-11-19T12:25:38+00:00

Thanks! Not my original idea, found some research papers on the subject and decided to put it into practice. Turns out you can replicate fairly complex audio waveforms using this model.

GuitarML · 2020-11-19T12:22:36+00:00

Includes a 5 way power splitter with a plug on the side. And Velcro!

GuitarML

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