Good morning 
Can you pass me a link to something confirming your claim, as Im very intrigued by the idea myself, and would love to know more.



 apytorch:

Previously I had used a couple LSTM layers with Keras for the “outer” part, but I’m intrigued by the current findings replacing LSTMs with CNN.


One of the key elements of CNN’s is that they excel where data have a grid-like structure. For images the grid is the distance between pixels and for time-series its the constant data sample rate. So it might be a problem using two sample rates as the CNN might have trouble when grid structure are different from sequence to sequence.



 apytorch:

My pared-down dataset is about 70GB in size, with ~2500 recordings (samples), that are of various lengths and each recorded at a different rate.


If you loose information at 128 Hz, is it possible to resample data to 512 Hz then?
Another solution could be to simple clip the data sequences to have same length but that would throw away a lot of otherwise usefull data.
You might also be able to train one CNN on the short sequences and another CNN on the long sequences and combine them into one classification using some sort of feature fusion? Like in the picture:
                    
            
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If none of these approaches work im afraid i cannot think of anything else than padding 
Cheers.