What I'd do:
Transform data into a representation that maximizes "similarity" of chunks that are otherwise "alike" but have large Euclidean distance in terms of raw waveforms
Apply similarity measure at each point in time
Extract indices of peak similarities
My transform of choice would be wavelet scattering, which is sparse, robust to ...
Is it possible to select the post-autocorrelation band-pass filtering parameters to get both these signals to match?
No, since forming the autocorrelation function of a real signal is going to do squaring operations on the spectrum, you can't apply a linear filter to implement the same nonlinear distortion.