Music genre classification via joint sparse low-rank representation of audio features

Panagakis, Yannis ORCID logoORCID:, Kotropoulos, Constantine L. and Arce, Gonzalo R. (2014) Music genre classification via joint sparse low-rank representation of audio features. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22 (12) . pp. 1905-1917. ISSN 2329-9290 [Article] (doi:10.1109/TASLP.2014.2355774)

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A novel framework for music genre classification, namely the joint sparse low-rank representation (JSLRR) is proposed in order to: 1) smooth the noise in the test samples, and 2) identify the subspaces that the test samples lie onto. An efficient algorithm is proposed for obtaining the JSLRR and a novel classifier is developed, which is referred to as the JSLRR-based classifier. Special cases of the JSLRR-based classifier are the joint sparse representation-based classifier and the low-rank representation-based one. The performance of the three aforementioned classifiers is compared against that of the sparse representation-based classifier, the nearest subspace classifier, the support vector machines, and the nearest neighbor classifier for music genre classification on six manually annotated benchmark datasets. The best classification results reported here are comparable with or slightly superior than those obtained by the state-of-the-art music genre classification methods.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 23763
Notes on copyright: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Yannis Panagakis
Date Deposited: 06 Mar 2018 17:16
Last Modified: 29 Nov 2022 23:16

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