And for the final bit of testing (or things I have to test for now), here is that comparison.
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So this is 31d PCA’d MFCC (raw/unnormalized) on the top and 28d spectralshape on the bottom:
Pretty close, but I have to say I think the MFCC is doing a bit better here.
Here’s the time series comparison:
The “dynamic range” of the spectralshape is a touch better, but the smoothness of the ramps looks better for the PCA’d MFCCs.
Given the reduced “dynamic range”, I wanted to try and see how these actually look if I scale/normalize the range a bit.
That gives me a time plot that looks like this:
Which I then paired with a more contextual assessment. Which sounds better. Or rather, which has a smoother trajectory when sonified in this way.
Here are the results:
If I close my eyes and just listen, the PCA’d MFCCs sound a lot more smooth in the transitions. The spectralshape one, although looking a bit smoother sometimes, tends to jump/stick to values more it seems.
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The effectiveness of these PCA’d MFCCs made me wonder how they stack up in terms of classification accuracy. This is something I had actually tried years ago and got absolutely dogshit results, but as outlined earlier in this thread, I think the normalization post PCA-ing was just breaking the relationship between the MFCC coefficients.
So I plugged this PCA-ing into my accuracy test and got the following results (inserted into the data from earlier today):
4 classes:
pca’d mfcc - 97.22% (my best results so far!)
mfcc baseline - 95.8333% (my previous “gold standard”)
spectral baseline - 87.5%
spectral no loudness - 87.5%
spectral no deriv - 88.88%
10 classes:
pca’d mfcc - 87.5%
mfcc baseline - 86.11%
spectral baseline - 66.66%
spectral no loudness - 66.66%
spectral no deriv - 72.22%
A slight improvement, but an improvement nonetheless. And this is using fluid.knnclassifier~, whereas I got better results using fluid.mlpclassifier~ before.
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So it seems that overall, PCA’d MFCCs capture the most variance here and still capture good transitional/interpolation states. The spectralshape stuff was very promising (though I did dread having to add a whole new descriptor “type” to SP -Tools), but not quite as good as just (further) refined MFCCs.