Difference between revisions of "MIR workshop 2008"

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(potential workshop titles)
(potential software, libraries, examples)
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* CLAM
 
* CLAM
 
* Machine Learning Libraries
 
* Machine Learning Libraries
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== Abstract ==
 +
This tutorial will target students, researchers, and industry audio engineers who are unfamiliar with the field of Music Information Retrieval (MIR).  We will demonstrate the myriad of exciting technologies enabled by the fusion of basic signal processing techniques with machine learning.      The presentation will be a high-level, applied, multimedia-rich, overview of the building blocks of MIR systems.  Our goal is to make highly-interdisciplinary technologies and dauntingly-complex algorithms approachable.      In the spirit of modern cooking shows, we will perform numerous demonstrations, including on-the-fly coding of basic intelligent audio" systems, prepared system demonstrations, and prepared audio examples demonstrating more complex systems.   
  
  
 
[[Category: Workshops]]
 
[[Category: Workshops]]

Revision as of 18:13, 4 March 2008

CCRMA Workshop: Music Information Retrieval

This is Jay and Ge's brainstorming page for this summer's MIR workshop.

logistics

  • Summer 2008
  • Instructors: Jay LeBoeuf and Ge Wang


potential workshop titles

  • Music Information Retrieval
  • Information Retrieval in the Service of Music
  • Music Information Retrieval and Applications for Computer Audio
  • Intelligent Audio Systems : A review of the foundations and applications of Semantic Audio Analysis and Music Information Retrieval

workshop outline

  • (example bullet)
    • (example sub-bullet)


potential software, libraries, examples

  • MATLAB
  • ChucK / UAna
  • Marsyas
  • CLAM
  • Machine Learning Libraries

Abstract

This tutorial will target students, researchers, and industry audio engineers who are unfamiliar with the field of Music Information Retrieval (MIR). We will demonstrate the myriad of exciting technologies enabled by the fusion of basic signal processing techniques with machine learning. The presentation will be a high-level, applied, multimedia-rich, overview of the building blocks of MIR systems. Our goal is to make highly-interdisciplinary technologies and dauntingly-complex algorithms approachable. In the spirit of modern cooking shows, we will perform numerous demonstrations, including on-the-fly coding of basic intelligent audio" systems, prepared system demonstrations, and prepared audio examples demonstrating more complex systems.