I have been working over the past days to compare mfcc data and try to use them to find nearest neighbors with fluid.dataset.
I compare the fluid.mfcc with the results I get from Carmine Cella’s mfccs (latest available distribution of orchidea) and I must say, his lead to better results (I’m using both with comparable FFT settings and 20 components).
In order to explain what I observe, I made a 5 minute video, I’m also going the share with Carmine.
With flucoma, I’m using median average over the entire sound. I compare it with Carmines averaged mfcc 20. I also compare it to my division of the sound into a few chunks (right now actually 6, compared to the 4 from a few days ago)
You will see that method in the video on the upper right.
But after a lot of testing I find that Carmine’s ‘static’ mfcc, which does somehow take the energy into account, leads to even better searches of neighbors, than splitting the sound into chunks.
As I’m not sure if FloComa is actively talking to Carmine, I just send this ‘observation’ to both of you and look forward to any thoughts, input etc.