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International Conference on 5G for Future Wireless Networks

5GWN 2019: 5G for Future Wireless Networks pp 86–95Cite as

Alarm Sound Recommendation Based on Music Generating System

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Abstract

In this paper, we propose an alarm sound recommendation system based on music generation. The recommendation system will be integrated with an application named iSmile, which is a sleep analysis and depression detection application built by the authors in previous work. We use a music generating algorithm based on GAN (Generative Adversarial Nets) as the core of the recommendation system. To the best of our knowledge, it is the first application recommending real-time generated music rather than existing music. In the following part of the paper, we detail the algorithm, the experiment we conducted and the result analysis. The result shows that the recommendation system can effectively generate and recommend proper alarm sound according to the emotion prediction.

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Author information

Authors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China

    Wenhan Han & Xiping Hu

  2. The Chinese University of Hong Kong, Sha Tin, Hong Kong

    Wenhan Han & Xiping Hu

Authors
  1. Wenhan Han
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  2. Xiping Hu
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Corresponding author

Correspondence to Wenhan Han .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada

    Victor C. M. Leung

  2. University of Science and Technology, Beijing, China

    Haijun Zhang

  3. Shenzhen University, Shenzhen, China

    Xiping Hu

  4. National University of Defense Technology, Changsha, China

    Qiang Liu

  5. Shizuoka University, Shizuoka, Japan

    Zhi Liu

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Cite this paper

Han, W., Hu, X. (2019). Alarm Sound Recommendation Based on Music Generating System. In: Leung, V., Zhang, H., Hu, X., Liu, Q., Liu, Z. (eds) 5G for Future Wireless Networks. 5GWN 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-030-17513-9_7

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  • DOI: https://doi.org/10.1007/978-3-030-17513-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17512-2

  • Online ISBN: 978-3-030-17513-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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