Music services, media players and managers provide support for content
classification and access based on filtering metadata values, statistics of access and user
ratings. This approach fails to capture characteristics of mood and personal history that
are often the deciding factors when creating personal playlists and collections in music.
This dissertation work presents MusicWiz, a music management environment that
combines traditional metadata with spatial hypertext-based expression and automatically
extracted characteristics of music to generate personalized associations among songs.
MusicWiz’s similarity inference engine combines the personal expression in the
workspace with assessments of similarity based on the artists, other metadata, lyrics and
the audio signal to make suggestions and to generate playlists. An evaluation of
MusicWiz with and without the workspace and suggestion capabilities showed
significant differences for organizing and playlist creation tasks. The workspace features
were more valuable for organizing tasks, while the suggestion features had more value
for playlist creation activities.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2010-08-8185 |
| Date | 2010 August 1900 |
| Creators | Meintanis, Konstantinos A. |
| Contributors | Shipman, Frank M. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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