Hi all, Thanks a lot for all these hints.
Cheers, Pierre. 2016-07-21 15:18 GMT+02:00 James Bullock <jamie.bull...@bcu.ac.uk>: > > 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 <g...@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 <lorenzofsut...@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@lists.iem.at mailing list > >>> UNSUBSCRIBE and account-management -> > https://lists.puredata.info/listinfo/pd-list > >>> > >> > >> _______________________________________________ > >> Pd-list@lists.iem.at mailing list > >> UNSUBSCRIBE and account-management -> > https://lists.puredata.info/listinfo/pd-list > > > > _______________________________________________ > > Pd-list@lists.iem.at mailing list > > UNSUBSCRIBE and account-management -> > https://lists.puredata.info/listinfo/pd-list > > _______________________________________________ > Pd-list@lists.iem.at mailing list > UNSUBSCRIBE and account-management -> > https://lists.puredata.info/listinfo/pd-list >
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