@Morphoice The basics are they are using ML models to analyze and replicate the behavior of analog equipment. They run various test signals through them and use the results to train a model that can predict how the device will respond to any input signal.
The Kemper Profiler uses a different approach. They capture the full response of an amp or effect at different settings. It creates a snapshot of the device and builds a profile. This is closer to convolution than ML.
The real problem you're going to run into with time-based dynamics (like a compressor with slow release) is the model will start to emphasize the non-linear behavior. The result of these ML compressors ends up sounding like saturators rather than compressors.
I'm on the same path as @griffinboy where I'm focusing on making models of specific components and inserting them between other analog models.
If you want to start tinkering with Neural models in HISE, here's a thread of my starting process:
https://forum.hise.audio/topic/8701/simple-ml-neural-network/108?_=1748359797127
@griffinboy also posted a thread about some details to the training scripts.
I haven't had time to fully dig in for the last few months, but plan to soon.
