International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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An Auto Playlist Generation System with One Seed Song

  • Bang, Sung-Woo (Department of Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Jung, Hye-Wuk (Department of Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Kim, Jae-Kwang (Department of Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Lee, Jee-Hyong (Department of Electrical and Computer Engineering, Sungkyunkwan University)
  • Received : 2009.11.27
  • Accepted : 2010.01.10
  • Published : 2010.03.25
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Abstract

The rise of music resources has led to a parallel rise in the need to manage thousands of songs on user devices. So users have a tendency to build playlist for manage songs. However the manual selection of songs for creating playlist is a troublesome work. This paper proposes an auto playlist generation system considering user context of use and preferences. This system has two separated systems; 1) the mood and emotion classification system and 2) the music recommendation system. Firstly, users need to choose just one seed song for reflecting their context of use. Then system recommends candidate song list before the current song ends in order to fill up user playlist. User also can remove unsatisfied songs from the recommended song list to adapt the user preference model on the system for the next song list. The generated playlists show well defined mood and emotion of music and provide songs that the preference of the current user is reflected.

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References

  1. John C. Platt, C. Burges, S. Swenson, C. Weare and A. Zheng,“Learning a Gaussian Process Prior for Automatically Generating Music Playlists,” In Proc. NIPS, vol. 14, pp. 1425-1423, 2002.
  2. F. Deli`ege, T. B. Pedersen, “Using Fuzzy Lists for Playlist Management,” In Proc. MMM, pp. 198-209, 2007.
  3. R. E. Thayer, The Biopsychology of Mood and Arousal, Oxford University Press, 1989.
  4. L. R. Rabiner, Fundamentals of speech recognition, Prentice-Hall, 1993.
  5. Xi. Shao, C. Xu and M. S. Kankanhalli, “Unsupervised Classification of Music Genre Using Hidden Markov Model,” IEEE International Conference on Multimedia and Expo, vol. 3, pp. 2023-2026, 2004.
  6. K. Kim, D. Lee, T. Yoon and J. Lee, “A music Recommendation System based on Preference Analysis,” In Proc. ICADIWT, pp. 102-106, 2008.
  7. J. J. Aucouturier and Francois Pachet, “Music Similarity Measures: What’s the Use?,” International Symposium on Music Information Retrieval, pp. 157-163, 2002.
  8. S. V. Dongen, “Graph Clustering by Flow Simulation,” PhD thesis, University of Utrecht, 2000.
  9. http://jaudio.sourceforge.net

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