Machine listening: Acoustic interface with ART

Benjamin D. Smith, Guy E Garnett

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Recent developments in machine listening present opportunities for innovative new paradigms for computer-human interaction. Voice recognition systems demonstrate a typical approach that conforms to event oriented control models. However, acoustic sound is continuous, and highly dimensional, presenting a rich medium for computer interaction. Unsupervised machine learning models present great potential for real-time machine listening and understanding of audio and sound data. We propose a method for harnessing unsupervised machine learning algorithms, Adaptive Resonance Theory specifically, in order to inform machine listening, build musical context information, and drive real-time interactive performance systems. We present the design and evaluation of this model leveraging the expertise of trained, improvising musicians.

Original languageEnglish (US)
Title of host publicationIUI'12 - Proceedings of the 17th International Conference on Intelligent User Interfaces
Number of pages4
StatePublished - Apr 26 2012
Event2012 17th ACM International Conference on Intelligent User Interfaces, IUI'12 - Lisbon, Portugal
Duration: Feb 14 2012Feb 17 2012

Publication series

NameInternational Conference on Intelligent User Interfaces, Proceedings IUI


Other2012 17th ACM International Conference on Intelligent User Interfaces, IUI'12


  • Adaptive resonance theory
  • Artificial intelligence
  • Machine listening
  • Music
  • Unsupervised machine learning

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction

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