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General / Re: GuitarML Plugins For Pi-Stomp
« on: April 21, 2022, 10:38:22 AM »
Thanks for the feedback! Good observation on the TS, should probably add a general boost to make it act more like the pedal.
I'm planning on adding more models to Darkstar in the next release, basically just adding a dropdown menu to switch amp models. I want to keep it as simple as possible and not make it bogged down with generic EQ or other effects, since there's already so much available.
As for which amps/pedals to add, I'll be relying on the community for most of these. I have a couple of sources already but if there's anyone reading this that wants to contribute, you can email me at "smartguitarml@gmail.com" and we can go from there. Basically all I need are audio recordings from the amp/pedal, where a specific input signal is played through the device and the output is recorded. I can provide the input signal in the form of a WAV file. If you have an audio interface, it's pretty easy to play the input WAV through your device and record the output simultaneously, either by direct out or from a microphone.
I'll be adding single parameter models to Darkstar, typically a gain/drive. These models require 5 separate recordings, so one 3-minute recording for each 0%, 25%, 50%, 75%, and 100% of a gain knob. I can then use these to train a neural network model that *hopefully* sounds close to the real thing.
Edit: I should note that this modelling process works for distortion/overdrive pedals, clean to overdriven amps, but does not capture reverb/delay/flange/phaser, etc. I have had good results with saturation effects, and mixed results with compressors.
I'm planning on adding more models to Darkstar in the next release, basically just adding a dropdown menu to switch amp models. I want to keep it as simple as possible and not make it bogged down with generic EQ or other effects, since there's already so much available.
As for which amps/pedals to add, I'll be relying on the community for most of these. I have a couple of sources already but if there's anyone reading this that wants to contribute, you can email me at "smartguitarml@gmail.com" and we can go from there. Basically all I need are audio recordings from the amp/pedal, where a specific input signal is played through the device and the output is recorded. I can provide the input signal in the form of a WAV file. If you have an audio interface, it's pretty easy to play the input WAV through your device and record the output simultaneously, either by direct out or from a microphone.
I'll be adding single parameter models to Darkstar, typically a gain/drive. These models require 5 separate recordings, so one 3-minute recording for each 0%, 25%, 50%, 75%, and 100% of a gain knob. I can then use these to train a neural network model that *hopefully* sounds close to the real thing.
Edit: I should note that this modelling process works for distortion/overdrive pedals, clean to overdriven amps, but does not capture reverb/delay/flange/phaser, etc. I have had good results with saturation effects, and mixed results with compressors.