Edit: Please treat code here as python-esque pseudo-code; it would syntactically fall closest to MATLAB or Python+numpy+scipy users.
If I have a signal in frames, and I want to put it back together - I just add each frame back into into a growing vector at a distance of
hop from the end. More specifically, I use
hop * frame_index.
Now I understand that this is naive; I believe ideally, I would satisfy a
cola condition based on the
If I have frames, and just want to put them back together (and in particular I don't need the spectral benefits of other windows - I don't plan to go spectral) - Am I right to understand that a rectangular window always satisfies cola, so, that's all I should need here.
BUT, for a rectangular window, I would expect some gain in power for the
n_overlap samples in each window - because the redundant samples get doubled. I thought this is why you needed a
So my big question here is:
Is this understanding flawed? and if not, then how would I go about performing 2 or 3 time varying operations on a signal that each work better for different
Side-Quest: When I look into reconstructing from frames, I always come back to putting an STFT back together, or OLA convolution. Is there some convolution approach that just puts the signal back together perfectly from its frames - It makes sense that something like this could exist, but I don't fully understand how.