Unsupervised Play: Machine Learning Toolkit for Max

نویسندگان

  • Benjamin D. Smith
  • Guy E. Garnett
چکیده

Machine learning models are useful and attractive tools for the interactive computer musician, enabling a breadth of interfaces and instruments. With current consumer hardware it becomes possible to run advanced machine learning algorithms in demanding performance situations, yet expertise remains a prominent entry barrier for most would-be users. Currently available implementations predominantly employ supervised machine learning techniques, while the adaptive, self-organizing capabilities of unsupervised models are not generally available. We present a free, new toolbox of unsupervised machine learning algorithms implemented in Max 5 to support real-time interactive music and video, aimed at the non-expert computer artist.

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تاریخ انتشار 2012