# Training for real-time NMF (fluid.nmfmatch~)

**URL:** https://discourse.flucoma.org/t/training-for-real-time-nmf-fluid-nmfmatch/112
**Category:** Usage Questions
**Created:** [November 6, 2018, 4:18pm UTC](https://discourse.flucoma.org/t/training-for-real-time-nmf-fluid-nmfmatch/112 "2018-11-06T16:18:00Z")
**Posts on this page:** 1
**Showing post:** 52

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### Author: ![jamesbradbury](https://discourse.flucoma.org/user_avatar/discourse.flucoma.org/jamesbradbury/32/1252_2.png) [@jamesbradbury](https://discourse.flucoma.org/u/jamesbradbury)
#### Post date: [December 17, 2018, 3:23pm UTC](https://discourse.flucoma.org/t/training-for-real-time-nmf-fluid-nmfmatch/112/52 "2018-12-17T15:23:24Z")

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> [@rodrigo.constanzo](#):
>
> Also noticed that you’re calculating the `resynthbuf` and `envbuf` , which aren’t needed unless you want them. (unless I’m missing something, you only need `filterbuf` for this kind of matching)

Correct. I just did this to make it clear and possibly useable in another context where you do need that info.

The whole [debounce issue](http://discourse.flucoma.org/t/debounce-time-0-0-slice-can-ignore-entries/) is another kettle of fish which can be addressed over there but an interesting find nonetheless.

> [@rodrigo.constanzo](#):
>
> Ok, something weird is happening. I managed to get the patch working one time (while testing with different settings). It wasn’t super accurate, but it was producing somewhat useful results. Since that point, I can’t seem to recreate it working. With your default patch I can’t get it working either (from a fresh download). `fluid.nmfmatch~` returns lists of `0.` .

I ran into similar problems while building the patch and I’m not sure what caused it. At first it seemed to be related to @maxrank on `nmfmatch~` requiring that it be set to the at least the amount of buffers inside the master buffer containing the multichannel storage of $1\_nmf\_filter buffers.

> [@rodrigo.constanzo](#):
>
> I’ll have a further play with this later today, and see if I can get a well working output (post `fluid.nmfmatch~` ) stage going.
> 
> (will also test with my “manual” batch processing patch posted above, which requires you to pre-crop your samples first)
> 
> I’m also wondering if there’s a way to apply some kind of median filter or percentile selection for multiple dicts as a good use case for training a system like this would be to tell it you’re playing a certain sound, and then doing so (perhaps several times). If you accidentally record a wrong sound, or if one particular sound is way out of place for some reason, to be able to groom that out, statistically. (e.g I’m training a classifier by hitting a specific drum, on one of the hits I miss and hit the wrong section)

Multiple data sets for each hit could be interesting. One strategy (that definitely is not suited to this patch) is to have a training phase where you hit multiple times and it averages out the nmf\_filter buffers. They should all be the same length and so the math would be quite easy. Perhaps this is another application for `fluid.bufcompose~` @tremblap ?

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