Hehe, hopefully have a few more once I get my head back in gear.
I tried this “gaffa” approach with the LTEp idea, but that lost all its momentum when I wanted to change a bit of the recipe based on @weefuzzy’s suggestion of using lower order MFCCs to reduce noise (which itself led to this thread). Having to rework the whole analysis pipeline to change one aspect of it was terrifying and I kind of stopped. That’s why I wanted to take a more rigorous approach as it’s not so easy to try “a bit of this and a bit of that”, since each one of those recipe bits takes me over an hour of really intense and fiddly coding to sort out (counting indices/channels and such). It’s the opposite of a ‘playful’ exploration.
Hence wanting to try the PCA->UMAP approach so what’s in there doesn’t matter so much (for this specific use case). I do like the idea of having a perceptually and philosophically meaningful descriptor space, but that may not always correspond with the most well represented space.
Either way I’d be baking in more descriptors, ala @balintlaczko, by including amount of onsets, time centroid, “timeness”, and potentially other bespoke descriptors.
Some of this does circle back to older concerns of how to search this data too. Like it would be great to search (or bias/reduce) by a custom descriptor (e.g. “timeness” > 0.6, onsets = 3, etc…) and then use a kdtree to find the nearest match from that subset (potentially not including “timeness” as one of its ingredients.
I did think of some kind of rotation or reorientation of the space afterwards. That wouldn’t be so bad as I could manually massage a mapping for each corpus and just have to do that once. That could prove to be impossible or problematic for reasons you’ve outlined (and more), but it’s a possibility.
Is there no known approach/best practice for further mapping/reorganizing a UMAP-ish projection? Or is it always meant to be “go explore the space with your mouse pointer” and see what is where!