Giving this a bit of a spiritual bump with no new concrete information/suggestions (I quite like and stand by my initial suggestions here).
As I mentioned in this thread I’ve been revisiting some patches and particularly when it gets into higher dimensional descriptor spaces, so much of what I’m doing is removing/pruning things I don’t want, and never wanted in the first place (every statistic of every descriptor I encounter along the way). So situations where I want the mean of spectral flatness, or the max of 20MFCCs, I now have significantly larger datasets which I have to extract data from, using only indices as there is no symbolic naming along the way either.
Also something I hadn’t really considered at the time of this original post, and was re-surprised to re-read in @tremblap’s explanation in this thread that having non-adjacent columns in a fluid.dataset~ means that it can be more expensive (including adding more interim steps/buffers/datasets) to extract the specific stats/descriptors you want because of how buffers are flattened. So not only is it significantly faffier (my main point for this thread), error prone, and expensive on the front end (presumably it’d be cheaper to only calculate what you need), there’s also another performance (and faff) hit on the tail end when you then have to sift out the stuff you only wanted in the first place.