Classification of music genres using sparse representations in overcomplete dictionaries

Rusu, Cristian Classification of music genres using sparse representations in overcomplete dictionaries. Journal of Control Engineering and Applied Informatics, 13 (1). pp. 13-42. ISSN 1454-8658 (2011)

Preview

PDF - Accepted Version Download (252kB) | Preview

Abstract

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.

Item Type:	Article
Funders:	The work has been funded by the Sectoral Operational Programme Human Resources Development 2007-2013 of the Romanian Ministry of Labour, Family and Social Protection through the Financial Agreement POSDRU/88/1.5/S/61178.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Research Area:	Computer Science and Applications
Depositing User:	Users 45 not found.
Date Deposited:	07 Mar 2013 12:49
Last Modified:	12 Mar 2013 14:58
URI:	http://eprints.imtlucca.it/id/eprint/1524

Actions (login required)

Edit Item