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@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@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.
>>>
>>>
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