| Title: | iTunes automatic music classifier & scribbler |
| Duration: | One Semester |
| Responsible TA: | Nicolas Jones |
| Goals: | Classify music (and, possibly, recommend better music or generate automatic playlists) |
| Keywords: | iTunes, preference elicitation |
| Abstract: |
iTunes already incorporates a rating mechanisms, a genre classification, a playlists categorisation, and a play-count log. Unfortunately, this information is very rarely used by iTunes to represent data in a semi-automatic way. Furthermore, a majority of users don’t use these at all, and the small subset of those who do spend a lot of time with it. We propose to write a plugin who will rely on the users’ implicit preferences (behaviour) to rate the songs, classify them by similarity in automated playlists, and possibly creating an improved online musical profile on sites like Last.fm. |
| Project Results: |
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| Required Skills |
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