# Onset-based regression (JIT-MFCC example)

**URL:** <https://discourse.flucoma.org/t/onset-based-regression-jit-mfcc-example/464>\
**Category:** Pre-Release Toolbox2 New Ideas\
**Created:** [April 26, 2020, 2:51pm UTC](https://discourse.flucoma.org/t/onset-based-regression-jit-mfcc-example/464 "2020-04-26T14:51:09Z")\
**Posts on this page:** 1\
**Showing post:** 49

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**Author:** ![rodrigo.constanzo](https://discourse.flucoma.org/user_avatar/discourse.flucoma.org/rodrigo.constanzo/32/12_2.png) [@rodrigo.constanzo](https://discourse.flucoma.org/u/rodrigo.constanzo)\
**Post date:** [June 27, 2020, 12:38pm UTC](https://discourse.flucoma.org/t/onset-based-regression-jit-mfcc-example/464/49 "2020-06-27T12:38:49Z")

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I decided to revisit this, as I’ve learned a bunch from the melbands range and smoothing/reduction in [this thread](https://discourse.flucoma.org/t/spectral-compensation/299/100), as well as some problematic fft settings in [this thread](https://discourse.flucoma.org/t/fluid-bufmelbands-not-outputting-bands-at-certain-fft-settings/525).

After some quick initial testing (took me a bit to figure out the new syntax/messaging) I still get better results from the MFCCs vs the melbands. I did, however, now get good results from the MFCCs when using a smaller analysis window of 256 (using @weefuzzy’s suggestion of `@fftsettings 256 64 512`). I think I get _slightly_ better matching using a larger analysis window of 512 but the tradeoff in latency isn’t worth it.

So at this point it’s looking like I’m going to be extracting every bit of juice of those 256 samples (“normal” descriptor stuff/stats, 40 melbands, 12mfccs+stats), each for a separate functions later on.

> [@tremblap](#):
>
> _[Rod question about “updating” the training points]_ - Yes you can with machine learning. Soon. You train, add points, train with problem point and/or tweaking parameters … Maybe the one after. We are devising packages. stay tuned!

Also wanted to give a bump to this section. I don’t remember any specific mention of the semi-supervised tweaking/updating in the last release(s), but the objects have been refactored, so it’s possible it could have happened and I didn’t notice.

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