Music playlist generation by assimilating GMMs into SOMs

被引：8

作者：

Balkema, Wietse ^{[1
]}

van der Heijden, Ferdi ^{[1
]}

机构：

[1] Univ Twente, Signals & Syst Grp, Fac Elect Engn Math & Comp Sci, Enschede, Netherlands

来源：

PATTERN RECOGNITION LETTERS | 2010年 / 31卷 / 11期

关键词：

Music playlists; Self organization; Gaussian mixtures; Genre classification; Earth mover's distance;

D O I：

10.1016/j.patrec.2010.02.001

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A method for music playlist generation, using assimilated Gaussian mixture models (GMMs) in self organizing maps (SOMs) is presented. Traditionally, the neurons in a SUM 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. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：1396 / 1402

页数：7

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