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Compact feature subset-based multi-label music categorization for mobile devices

Authors
Lee, JaesungSeo, WangdukPark, Jin-HyeongKim, Dae-Won
Issue Date
Feb-2019
Publisher
Springer New York LLC
Keywords
Hybrid search; Mobile devices; Multi-label learning; Music information retrieval
Citation
Multimedia Tools and Applications, v.78, no.4, pp 4869 - 4883
Pages
15
Journal Title
Multimedia Tools and Applications
Volume
78
Number
4
Start Page
4869
End Page
4883
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/3388
DOI
10.1007/s11042-018-6100-8
ISSN
1380-7501
1573-7721
Abstract
Music categorization based on acoustic features extracted from music clips and user-defined tags forms the basis of recent music recommendation applications, because relevant tags can be automatically assigned based on the feature values and their relation to tags. In practice, especially for handheld lightweight mobile devices, there is a certain limitation on the computational capacity, owing to consumers’ usage behavior or battery consumption. This also limits the maximum number of acoustic features to be extracted, and results in the necessity of identifying a compact feature subset that is used for the music categorization process. In this study, we propose an approach to compact feature subset-based multi-label music categorization for mobile music recommendation services. Experimental results using various multi-labeled music datasets reveal that the proposed approach yields better performance when compared to conventional approach. © 2018 Springer Science+Business Media, LLC, part of Springer Nature
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소프트웨어대학 (소프트웨어학부)
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