TY  - JOUR
Y1  - 2011///
SP  - 13
A1  - Rusu, Cristian
SN  - 1454-8658
EP  - 42
VL  - 13
UR  - http://www.ceai.srait.ro/index.php/ceai/article/view/1159
IS  - 1
N2  - 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.
JF  - Journal of Control Engineering and Applied Informatics
AV  - public
ID  - eprints1524
TI  - Classification of music genres using sparse representations in overcomplete dictionaries
ER  -