Abstract
A method for music playlist generation, using assimilated Gaussian mixture models (GMMs) in self organizing
maps (SOMs) is presented. Traditionally, the neurons in a SOM are represented by vectors, but in
this paper we propose to use GMMs instead. To this end, we introduce a method to adapt a GMM such
that its distance to a second GMM decreases at a controllable rate. Self organization is demonstrated
using a small music database and a music classification task.
Original language | Undefined |
---|---|
Pages (from-to) | 1396-1402 |
Number of pages | 7 |
Journal | Pattern recognition letters |
Volume | 31 |
Issue number | 11 |
DOIs | |
Publication status | Published - Aug 2010 |
Keywords
- EWI-18345
- Self organization
- Earth mover’s distance
- IR-72797
- Gaussian mixtures
- Music playlists
- METIS-271002
- Genre classification