• Title/Summary/Keyword: Playlist generator

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An Auto Playlist Generation System with One Seed Song

  • Bang, Sung-Woo;Jung, Hye-Wuk;Kim, Jae-Kwang;Lee, Jee-Hyong
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.10 no.1
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    • pp.19-24
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    • 2010
  • 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.

A Playlist Generation System based on Musical Preferences (사용자의 취향을 고려한 음악 재생 목록 생성 시스템)

  • Bang, Sun-Woo;Kim, Tae-Yeon;Jung, Hye-Wuk;Lee, Jee-Hyong;Kim, Yong-Se
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.3
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    • pp.337-342
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    • 2010
  • The rise of music resources has led to a parallel rise in the need to manage thousands of songs on user devices. So users are tend to build play-list for manage songs. However the manual selection of songs for creating play-list is bothersome task. This paper proposes an auto play-list recommendation system considering user's context of use and preference. This system has two separate systems: mood and emotion classification system and music recommendation system. Users need to choose just one seed song for reflection their context of use and preference. The system recommends songs before the current song ends in order to fill up user play-list. User also can remove unsatisfied songs from recommended song list to adapt user preferences of the system for the next recommendation precess. The generated play-lists show well defined mood and emotion of music and provide songs that user preferences are reflected.