IEEE TCDL Bulletin
 
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TCDL Bulletin
Volume 3   Issue 2
Summer 2007

 

On-demand Metadata Extraction Network (OMEN)

Ichiro Fujinaga
Schulich School of Music, McGill University
555 Sherbrooke West
Montreal, QC H3A 1E3
+1 514 398-4535 x00944
<ich@music.mcgill.ca>
Daniel McEnnis
Schulich School of Music, McGill University
555 Sherbrooke West
Montreal, QC H3A 1E3
+1 514 398-4535 x0300
<daniel.mcennis@mail.mcgill.ca>
 
OMEN (On-demand Metadata Extraction Network) addresses a fundamental problem in Music Information Retrieval research: the lack of universal access to a large dataset containing significant amounts of copyrighted music. This is accomplished by utilizing the large collections of digitized music available at many libraries. Using OMEN, libraries will be able to perform on-demand feature extraction on site, returning feature values to researchers instead of providing direct access to the recordings themselves. This avoids copyright difficulties, since the underlying music never leaves the library that owns it. The analysis is performed using grid-style computation on library machines that are otherwise under-utilized (e.g., devoted to patron web and catalogue use). An example of this system is hosted by the DDMAL Lab at McGill University's Music Technology Area.

Thumbnail image of poster

For a larger view of Figure 1, click here.

 

© Copyright 2007 Ichiro Fujinaga and Daniel McEnnis
Some or all of these materials were previously published in the Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital libraries, ACM 1-59593-354-9.

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