Classification of music genres using sparse representations in overcomplete dictionaries

被引：0

作者：

Rusu, Cristian ^{[1
]}

机构：

[1] Univ Politehn Bucuresti, Fac Automat Control & Comp, Bucharest, Romania

来源：

CONTROL ENGINEERING AND APPLIED INFORMATICS | 2011年 / 13卷 / 01期

关键词：

music genre classification; linear predictive coding; sparse representations; orthogonal matching pursuit; K-SVD;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a simple, but efficient and robust, method for music genre classification that utilizes sparse representations in overcomplete dictionaries. The training step involves creating dictionaries, using the K-SVD algorithm, in which data corresponding to a particular music genre has a sparse representation. In the classification step, the Orthogonal Matching Pursuit (OMP) algorithm is used to separate feature vectors that consist only of Linear Predictive Coding (LPC) coefficients. The paper analyses in detail a popular case study from the literature, the ISMIR 2004 database. Using the presented method, the correct classification percentage of the 6 music genres is 85.59%, result that is comparable with the best results published so far.

引用

页码：35 / 42

页数：8

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