So I went ahead and created a patch that does some of what we talked about in the last chat and made a quick video demo-ing it.
So I’ve analyzed a mix of a bunch of different samples for 4 descriptors with some statistics each, and then analyzed three time scales from the file.
The descriptors/stats are:
loudness_mean
loudness_derivative
loudness_deviation
centroid_mean
centroid_derivative
centroid_deviation
flatness_mean
flatness_derivative
flatness_deviation
rolloff_max
rolloff_derivative
rolloff_deviation
And the time series analyzed are 0-512samples, 0-4410samples, and the entire file.
I chose these as they more closely correspond with my “real-time” analysis window of 512 samples (so I can do like-for-like matching there), and for the Kaizo Snare performance I matched against the first 100ms of each file, and that provided musically useful results.
The results for each matching window is quite different, not surprisingly. I think given the material I like the 512 and 4410 options the most. The 512 gives the most range and surprise as it’s matching the initial 11ms very well, and the rest is a “surprise”. The whole file matching is dogshit, particularly with these samples, as there’s so much fading out during the file that you don’t get anything useful here.
For the variable part of the video I’m using the % matcher in entrymatcher here and crossfading between putting all the weight on the 512 query, or the 4410 query.
So based on this, I think that it would indeed be useful to be able to bias a query between multiple versions of the same n-dimensional space. Because everything is in the same scaling/dimensions/space, nothing gets fucked around like what would happen when trying to bias things towards “caring more about loudness” or “caring more about spectral shape”.
Musically, this makes a lot of sense to me, to be able to nudge a query, or match in a direction that musically grounded, as opposed to selecting/massaging algorithms based on data science stuff.
I don’t really know what this would mean in practice, however. Simply exposing multipliers pre-matching would add some usefulness, like with @danieleghisi mentioned here, though things get complicated really quickly once you have a lot of dimensions and/or data scaling/sanitizing.