OMEN (On-demand Metadata Extraction Network) addresses a fundamental problem in Music Information Retrieval: the lack of universal access to a large dataset containing significant amounts of copyrighted music. This thesis proposes a solution to this problem that 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-used (e.g., devoted to patron web and catalogue use).
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.99382 |
| Date | January 2006 |
| Creators | McEnnis, Daniel. |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Master of Arts (Schulich School of Music.) |
| Rights | © Daniel McEnnis, 2006 |
| Relation | alephsysno: 002570784, proquestno: AAIMR28567, Theses scanned by UMI/ProQuest. |
Page generated in 0.0161 seconds