Unifying low-level and high-level music similarity measures
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- dc.contributor.author Bogdanov, Dmitryca
- dc.contributor.author Serrà Julià, Joanca
- dc.contributor.author Wack, Nicolasca
- dc.contributor.author Herrera Boyer, Perfecto, 1964-ca
- dc.contributor.author Serra, Xavierca
- dc.date.accessioned 2018-06-01T08:55:40Z
- dc.date.available 2018-06-01T08:55:40Z
- dc.date.issued 2011
- dc.description.abstract Measuring music similarity is essential for multimedia retrieval. For music items, this task can be regarded as obtaining a suitable distance measurement between songs defined on a certain feature space. In this paper, we propose three of such distance measures based on the audio content: first, a low-level measure based on tempo-related description; second, a high-level semantic measure based on the inference of different musical dimensions by support vector machines. These dimensions include genre, culture, moods, instruments, rhythm, and tempo annotations. Third, a hybrid measure which combines the above-mentioned distance measures with two existing low-level measures: a Euclidean distance based on principal component analysis of timbral, temporal, and tonal descriptors, and a timbral distance based on single Gaussian Mel-frequency cepstral coefficient (MFCC) modeling. We evaluate our proposed measures against a number of baseline measures. We do this objectively based on a comprehensive set of music collections, and subjectively based on listeners' ratings. Results show that the proposed methods achieve accuracies comparable to the baseline approaches in the case of the tempo and classifier-based measures. The highest accuracies are obtained by the hybrid distance. Furthermore, the proposed classifier-based approach opens up the possibility to explore distance measures that are based on semantic notions.
- dc.description.sponsorship This work was supported in part by the FI Grant of Generalitat de Catalunya (AGAUR); in part by the Music 3.0 project of the Spanish Ministry of Industry, Tourism, and Trade (Avanza Contenidos, TSI070100-2008-318); and in part by the Buscamedia project (CEN-20091026).
- dc.format.mimetype application/pdf
- dc.identifier.citation Bogdanov D, Serrà J, Wack N, Herrera P, Serra X. Unifying low-level and high-level music similarity measures. IEEE Trans Multimedia. 2011;13(4):687-701. DOI: 10.1109/TMM.2011.2125784
- dc.identifier.doi http://dx.doi.org/10.1109/TMM.2011.2125784
- dc.identifier.issn 1520-9210
- dc.identifier.uri http://hdl.handle.net/10230/34780
- dc.language.iso eng
- dc.publisher Institute of Electrical and Electronics Engineers (IEEE)ca
- dc.relation.ispartof IEEE Transactions on Multimedia. 2011;13(4):687-701.
- dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/TSI070100-2008-318
- dc.rights © 2011 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. The final published article can be found at https://ieeexplore.ieee.org/document/5728926/
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.subject.keyword Distance measurement
- dc.subject.keyword Information retrieval
- dc.subject.keyword Knowledge acquisition
- dc.subject.keyword Multimedia computing
- dc.subject.keyword Multimedia databases
- dc.subject.keyword Music
- dc.title Unifying low-level and high-level music similarity measuresca
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/acceptedVersion