eprintid: 1524
rev_number: 9
eprint_status: archive
userid: 45
dir: disk0/00/00/15/24
datestamp: 2013-03-07 12:49:58
lastmod: 2013-03-12 14:58:11
status_changed: 2013-03-07 12:49:58
type: article
metadata_visibility: show
creators_name: Rusu, Cristian
creators_id: cristian.rusu@imtlucca.it
title: Classification of music genres using sparse representations in overcomplete dictionaries
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: public
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.
date: 2011
publication: Journal of Control Engineering and Applied Informatics
volume: 13
number: 1
pagerange: 13-42
refereed: TRUE
issn: 1454-8658
official_url: http://www.ceai.srait.ro/index.php/ceai/article/view/1159
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.
citation:   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)  
document_url: http://eprints.imtlucca.it/1524/1/Classification%20of%20music%20genres%20using%20sparse.pdf