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

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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.

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Acknowledgements

This research was supported by the Chung-Ang University Research Scholarship Grants in 2018 and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2016R1C1B1014774).

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Authors and Affiliations

  1. Chung-Ang University, Seoul, Republic of Korea

    Jaesung Lee, Wangduk Seo, Jin-Hyeong Park & Dae-Won Kim

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  1. Jaesung Lee
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  2. Wangduk Seo
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  3. Jin-Hyeong Park
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  4. Dae-Won Kim
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Correspondence to Dae-Won Kim.

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Lee, J., Seo, W., Park, JH. et al. Compact feature subset-based multi-label music categorization for mobile devices. Multimed Tools Appl 78, 4869–4883 (2019). https://doi.org/10.1007/s11042-018-6100-8

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  • DOI: https://doi.org/10.1007/s11042-018-6100-8

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