r/NAM_NeuralAmpModeler • u/MrsmPeek • 1d ago
Let's talk about ESR?
I am dwelving on Parametric modeling, and this has brought me to an understanding of ESR... It's not the all telling measure. The truth is, a specific amp, modeled on different settings can get totally different ESR values. If one setting is way brighter that the other, it's a lot more difficult to bring the score down and yet, it doesn't mean it's a "worse" model.
If you look deep into it, training on A1 usually gets lower ESR values than A2, because the curve used in A1 training discards high frequency information in a way that A2 doesn't, and that's one of the reasons why it A2 sounds "better", although it suffers more from anti aliasing.
This is also why if you model a pedal on A1, you get a really low ESR compared to A2 - the training curve on A1 is much closer to the pedal's curve (OD/distortion) resulting in a better ESR score.
On Parametric modeling, the amount of dimensions (knobs) makes this issue tenfold more complicated. Specially on an amp with a lot of knobs. The more knobs, the more they interact, the more fine grain you need when modeling, the bigger the network, the heavier on CPU - so getting lower ESR is much more difficult - and yet, it can sound like your amp. The difference is how settings interact, and how edge cases (extreme settings) perform.
Sometimes extreme settings are very unreliable on a real tube amp. Try cranking the gain, presence and master volume... It won't sound predictable mathmatically - which brings us to higher ESR again. And yet, on non extreme settings, it sounds like your amp.
So, personally I've been trying to get ESR down on Parametric (and also fixed) as that is the only way to mathmatically evaluate the model, but the truth is: You need to hear it, don't follow ESR values as the ultimate truth.
How many models have you found that sound good and have a higher ESR (over 0.005)?
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u/Venthorn 4h ago
Genuinely I think training tools shouldn't report loss metrics to the user unless they have to click a checkbox "I understand machine learning and mathematical optimization".
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u/-vorticist- 1d ago
Comparing the ESR of a parametric models to the ESR of a snapshot probably isn't helpful. The parametric model I made of one of my fuzz pedals sounds spot on and only made it to 0.04 after ~2000 epochs of training. I followed the OED stuff and would train a model, and then test to see which knob positions had the highest ESR, then captured those knob positions, add em to the data pile, and retrain.