Some have asked how this is different from what an impulse response convolution plugin can do. The simple answer is that IR convolution is a linear, time-invari

Some have asked how this is different from what an impulse response convolution plugin can do. The simple answer is that IR convolution is a linear, time-invariant system. It can adjust the amplitudes and phases of different frequencies, but it can't create new ones. What this is doing is basically a nonlinear generalization of that, which makes it better able to model overdrive, amp saturation, and so on. One thing I am curious about is exactly how this works. How do neural networks make black box nonlinear system modeling possible, and how does this relate to something like a Volterra series? Would love an explanation and/or some sources if anyone has any.