TooB ML Amplifier
The TooB ML Amplifier plugin provides emulation of a variety of amplifiers and overdrive pedals that are implemented using neural-network-based machine learning models of real amplifiers. Generally, TooB ML uses smaller and more efficient neural networks than does TooB Neural Amp Modeller. As a result, TooB ML models use significantly less CPU, although TooB Neural Amp Modeller is significantly more flexible, and has a huge number of downloadable models. The BluesJR model has an additional Gain control the is used by the BluesJR model to simulate the effect of Blues Junior's gain control. For convenience, tone controls are provided for rough shaping of tone before the input signal is fed to the neural net models. The tone section can be completely bypassed by setting all tone controls to exactly 0.5. The Sag section models transformer sag using DSP simulation, since transformer sag is not captured well by neural network models. A modest amount of transformer sag produces an amp simulation that feels more "forgiving". The Sag section is not based on neural network modelling. To disable transformer modelling, set the Sag Out and Sag Feedback controls to zero. The SagF control affects the cutoff frequency of a low-pass filter which is used to calculate the amount of transformer sag. Lower values produce a faster onset; higher values produce a slower onset. Nominally, SagF should be set to 13Hz, which loosely models the transformer sag of a Fender Bassman based on circuit simulations. The Sag Feedback control uses the calculated transformer voltage sag to scale the signal level of the input to the model. For models with gentle overdrive, it tends to keep the model operating in the sweet stop where the model is on the edge of breakup. It does not work particularly well for models of heavily overdriven amplifiers. The effect of Sag Out is similar to that of an analog compressor. Notionally, Sag Feedback models the effect of transformer sag on intermediate transformer stages (which causes them to clip at a lower level than they ordinarily would); and Sag Out models the effect of transformer sag on the final power stage of a guitar amplifier. The TooB ML Amplifier is based heavily on code ported from from the RTNeural project (https://github.com/jatinchowdhury18/RTNeural), by Jatin Chowdhury, and uses model files from the GuitarML Neural Pi project (https://github.com/GuitarML/NeuralPi).
Model gain parameter (used by BluesJR model only)
How much transformer sag affects output volume, post-model. Similar to an analog compressor effect.
Transformer sag feedback. How much transformer sag affects the input signal to the model. Tends to keep the model on the edge of breakup.