[PD] Machine learning and sound ?

[me.grimm]

Hi Jamie,

Compiled on OSX fine with normal *.pd_darwin files. How does one install? I
assume libs need to access things in other directories (DerovedData, etc)
that isn't apparent to me.

Opening help patch  for ml.svm in build -> macosx -> build -> Development
just produces "ml.svm... couldn't create" error...

Thanks!
m

On Thu, Jul 21, 2016 at 9:18 AM, James Bullock <Jamie.Bullock at bcu.ac.uk>
wrote:

>
> Hi all,
>
> To answer the OP’s question: yes it is possible to “do machine learning
> with sound” and yes you can use ml.lib and Pd for that.
>
> I would suggest using the upstream version of ml.lib, the version on the
> Cycling74 GitHub is a fork. Here’s the upstream:
> https://github.com/cmuartfab/ml-lib
>
> One of the problems with ml.lib is the documentation is very poor, we are
> addressing this, and soon there will be a full set of help files and
> possibly some examples. For now, sending an object the “help” message, you
> might be able to figure things out. Reading our NIME paper may also help:
> https://nime2015.lsu.edu/proceedings/201/0201-paper.pdf
>
> Machine learning is a very broad field, and in terms of “where to start”
> you might want to look at classification problems such as “out of a set of
> N known classes of sound, which one most closely matches sound X”. This is
> a well-studied problem, and you might want to start with a paper like this
> one: http://www.music.mcgill.ca/~ich/research/icmc00/icmc00.timbre.pdf
>
> It would be a useful exercise to replicate the Fujinaga MacMillan
> experiment (which did in fact originally use Pd) using ml.knn, or indeed
> their original knn external, which is still available in the Pd svn.
>
> Good luck!
>
> Jamie
>
>
>
> > On 21 Jul 2016, at 13:42, Thomas Grill <gr at grrrr.org> wrote:
> >
> > Please note that most applications of neural nets are non-realtime, e.g.
> not in the same domain as Pure Data.
> > The evaluation of neural networks can be, but the training never is.
> > best, Thomas
> >
> >> Am 21.07.2016 um 14:37 schrieb Lorenzo Sutton <lorenzofsutton at gmail.com
> >:
> >>
> >> On 21/07/2016 12:08, Pierre Massat wrote:
> >>> Dear List,
> >>>
> >>> I did a little bit of machine learning with neural network when I was
> in
> >>> school, and I'd like to try it on sounds. What I'd like to do is to
> >>> identify patterns, types of sounds, like "people talking", "loud,
> >>> compressed rock music", etc.
> >>
> >> If I understand correctly, maybe the keyword you're after is "automatic
> music classification"? (to which you could add e.g. "machine learning"
> "pure data" etc.).
> >> In this case there is loads of stuff... A good starting point (other
> than google) could be: http://www.ismir.net/society.html
> >>
> >> Hope this helps.
> >> Lorenzo.
> >>
> >>>
> >>> Is that feasible ? I found this library on the web :
> >>> https://github.com/Cycling74/ml-lib
> >>> But I have no clue how to use it.
> >>>
> >>> Do you have any suggestions on where to start ? Can I feed it sound
> >>> files ? Or do I need to extract some "indicators" from it (loudness,
> >>> spectrum, or something) ?
> >>>
> >>> Thanks in advance for your help !
> >>>
> >>> Cheers,
> >>>
> >>> Pierre.
> >>>
> >>>
> >>> _______________________________________________
> >>> Pd-list at lists.iem.at mailing list
> >>> UNSUBSCRIBE and account-management ->
> https://lists.puredata.info/listinfo/pd-list
> >>>
> >>
> >> _______________________________________________
> >> Pd-list at lists.iem.at mailing list
> >> UNSUBSCRIBE and account-management ->
> https://lists.puredata.info/listinfo/pd-list
> >
> > _______________________________________________
> > Pd-list at lists.iem.at mailing list
> > UNSUBSCRIBE and account-management ->
> https://lists.puredata.info/listinfo/pd-list
>
> _______________________________________________
> Pd-list at lists.iem.at mailing list
> UNSUBSCRIBE and account-management ->
> https://lists.puredata.info/listinfo/pd-list
>



-- 
____________________
m.e.grimm, m.f.a, ed.m.
syracuse u., tc3
megrimm.net
____________________
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.puredata.info/pipermail/pd-list/attachments/20160723/3b82d330/attachment-0001.html>