Compact feature subset-based multi-label music categorization for mobile devices
- Authors
- Lee, Jaesung; Seo, Wangduk; Park, Jin-Hyeong; Kim, 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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- Appears in
Collections - College of Software > Department of Artificial Intelligence > 1. Journal Articles
- College of Software > School of Computer Science and Engineering > 1. Journal Articles
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