tone generated by analog circuits is notoriously difficult to reproduce digitally. the conceptual behavior is often easy to model, but hardware deviates from theory in ways that are technically subtle but audibly apparent.

it's deterministic, but the parameters may be unknown and approximate values must often be discovered by iterative guess-and-check. researching and manually modeling an approximation can be incredibly tedious and still fall short. this is exactly the kind of application that machine learning excels at.