Hi guys, sorry for the delay in responding! I found interesting this idea
of using guitar samples to base the pitch/onset detection, but I think this
would limit a bit the idea of plugin to guitars only right? Plus, i don't
know anything about machine learning too hahaha, anyway, i think its a good
idea, but for this plugin I was trying to do something more general. To
Hermann, in this implementation I left the algorithm to choose at runtime,
the algorithms are briefly described in these links (at 'methods') :
Onset:
http://aubio.org/manpages/latest/aubioonset.1.html
Pitch:
http://aubio.org/manpages/latest/aubiopitch.1.html
I tested the plugin with both zynaddsubfx and a guitar, and in each
situation a pitch method worked better (some methods didn't recognize the
lowest frequencies), didn't feel much difference in the onset methods
variation. Have you guys tryed aubio lib yet?
Att,
Lucas
2014-05-11 22:44 GMT-03:00 Rafael Vega <email.rafa@email-addr-hidden>:
>
>
>
>>
>> I would be interested in trying a different approach to this problem.
>> Namely using machine learning. Would the guitar players on this list
>> be willing to provide training data? I.e. audio files of you playing
>> single notes with an additional text file describing the played
>> pitches. A format like
>>
>> frame-number start 440
>> frame-number end
>>
>> e.g. you have an audio clip at 1000hz sampling rate where an 440hz A
>> starts on frame 500 (at 0.5 secs) that lasts to frame 2500 (so, 2 secs
>> duration):
>>
>> 500 start 440
>> 2500 end
>>
>
> This would be an awesome project. I'm very interested. I can provide some
> training files and a bit of help coding or other supporting tasks. I don't
> know anything about machine learning, though.
>
>
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>
>
-- Lucas Conejero Takejame Engenharia Elétrica - ênfase em computação Escola Politécnica - USP
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Received on Tue May 13 00:15:02 2014
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