Tag: machine learning

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New papers

p>I am looking forward to SMC2012 in Copenhagen, Denmark, the 133rd AES Convention in San Francisco, USA, and the ACM Multimedia 2012 in Nara, Japan:

  • Peters N., Schacher J., Lossius T.: SpatDIF: Principles, Specification, and Examples, to appear in Proc. of the 9th Sound and Music Computing Conference (SMC), Copenhagen, Denmark, 2012.
  • Peters N., Lossius T., Place T.: An Automated Testing Suite for Computer Music Environments, to appear in Proc. of the 9th Sound and Music Computing Conference (SMC), Copenhagen, Denmark, 2012.
  • Peters N., Choi J., Lei H.: Matching artificial reverb settings to unknown room recordings: a recommendation system for reverb plugins, to appear at 133rd AES Convention, San Francisco, 2012.
  • Peters N., Lei. H., Friedland G.: Name That Room: Room identification using acoustic features in a recording, to appear at ACM Multimedia 2012, Nara, Japan, 2012.
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The ML semester

Bayes' theorem

Bayes’ theorem (from wikipedia)

The new school year has started here at UCB a few weeks ago. After some mellow summer weeks, the campus is now crowded with students again.

Motivated by recent publications which apply machine learning tools to room acoustics research (such as Shabtai et al. on room volume classification based on room impulse responses), I decided to extend my machine learning knowledge and attend the courses Introduction to Machine Learning with Stuart Russell and Statistical Learning Theory with Michael Jordan and Martin Wainwright. I’m going to use this new knowledge in a spatial sound classification project at the end of this year.