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 protected]>: > > > >> >> 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. > > > _______________________________________________ > Linux-audio-dev mailing list > [email protected] > http://lists.linuxaudio.org/listinfo/linux-audio-dev > > -- Lucas Conejero Takejame Engenharia Elétrica - ênfase em computação Escola Politécnica - USP
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