Show HN: GuitarLSTM-Create deep learning models of guitar amps/pedals in minutes
github.com
github.com
The purpose of this project is to improve on the previous WaveNet model built for the same task. Using LSTM is orders of magnitude faster and more accurate for emulating guitar signals than WaveNet. A real time guitar plugin exists for the WaveNet implementation, and a plugin for this the LSTM model is currently in work. Compare to Neural DSP’s Quad Cortex neural capture feature, which uses a similar technique on their $1600 floor modeler.
I'm a software engineer, but also a big guitarist. The WaveNet stuff is really interesting, but I'm not a DSP/ML expert. I could help you:
* Model a Dr. Z Maz 38 MkII, 5w Fender Tweed Champ, Ampeg SCR-DI preamp/DI
* Provide raw samples of: Gibson Les Paul 1958 Reissue (R8), Fender Telecaster (1955 type partscaster), Fender Stratocaster (1962 "thin skin" reissue), Gibson 1963 SG Junior Reissue, Fender 1958 Precision Bass Reissue, Taylor 814ce acoustic guitar
* Get the code installed on my local, test whatever you need help testing, etc.
* Testing and feedback on the real-time plugin i.e. using a VST/AU in Logic, testing latency, testing sound against in-room amp sound, etc.
I'd also be interested in figuring out how to get the real-time plugin you're developing into a pedal enclosure. I have a friend who is a top notch silicon valley hardware/power engineer, and we've been toying with the Strymon Iridium, Impulse Responses, analog pedal building, etc. It'd be interesting to use some guerrilla marketing, sell a few pedals on The Gear Page, and see what people think.
benstandefer (at) <google's email service> (dot) com