Aaaand for good measure I implemented the r2 output as well.
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Bd76b4cksvzDe7a9aey+O.Lb6pU.
-----------end_max5_patcher-----------
Here is a comparison between the same entries with and without the first frame, to see the impact it as on the r2 value.
With first frame:
(slope value isn’t great, but it’s in the “right” direction, but r2 value is pretty low given the overall shape)
Without the first frame:
(stronger slope value, and much higher r2, given the fit of the slope)
With first frame:
Without first frame:
And for a more oddball shape.
With the first frame:
Without first frame:
1 Like
Aaaaaaaaand here’s the code for doing it in the log (dB) domain.
----------begin_max5_patcher----------
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-----------end_max5_patcher-----------
1 Like
Moving this into here, so it’s not in the “public” part of the forum, and it also relates to this discussion in this thread.
I’m thinking about doing something similar-ish, or rather, trying to create macro descriptors. So chunking a bunch of loudness descriptors/stats (perhaps even per-frame) and reducing that down to 1 or 2 dimensions, doing the same with mfccs/descriptors/stats, and bringing that down etc…
For my analysis time frame, and general use case, pitch isn’t as important, so not sure what to do on that front, but the general idea being to reduce a mixed/large descriptor space into a lower amount of dimensions, but grouped by perceptually related sub categories…
Is the idea to give the KDTree at the end the same amount of entries per thing you find important (as well as scaling them appropriately)? So if the KDTree has 6 things in it, two per L, P, and T, that it should give them equal significance in finding the nearest point?