Article

Lyricon: A Visual Music Selection Interface Featuring Multiple Icons

Authors:

Wakako Machida,

Takayuki ItohAuthors Info & Claims

IV '11: Proceedings of the 2011 15th International Conference on Information Visualisation

Pages 145 - 150

https://doi.org/10.1109/IV.2011.62

Published: 13 July 2011 Publication History

Abstract

This paper presents "Lyricon", a technique that automatically selects multiple icons of tunes block-by-block, and effectively displays the icons. Here, Lyricon selects icons based on not only musical features, but also lyrical keywords. In other words, Lyricon can reflect not only the features of the tunes but also the story of lyrics on its icon selection. Users can understand both impression of the sounds and the content of the lyrics, and they can choose songs which is suitable for their feeling based on the visual impression of the icons. Besides, embedding Lyricon on GUIs of music players is convenient to play specific parts of songs.

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Lima HSantos CMeiguins B(2021)A Survey of Music Visualization TechniquesACM Computing Surveys10.1145/346183554:7(1-29)Online publication date: 18-Jul-2021
https://dl.acm.org/doi/10.1145/3461835
Zhao NKim NHerman LPfister HLau REchevarria JBylinskii ZBernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)ICONATE: Automatic Compound Icon Generation and IdeationProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376618(1-13)Online publication date: 21-Apr-2020
https://dl.acm.org/doi/10.1145/3313831.3376618
Uno AItoh TCho YShin SKim SHung CHong J(2014)MALLProceedings of the 29th Annual ACM Symposium on Applied Computing10.1145/2554850.2554861(939-944)Online publication date: 24-Mar-2014
https://dl.acm.org/doi/10.1145/2554850.2554861

Index Terms

Lyricon: A Visual Music Selection Interface Featuring Multiple Icons
1. Applied computing
  1. Arts and humanities
    1. Sound and music computing

Index terms have been assigned to the content through auto-classification.

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Published In

IV '11: Proceedings of the 2011 15th International Conference on Information Visualisation

July 2011

620 pages

ISBN:9780769544762

Publisher

IEEE Computer Society

United States

Publication History

Published: 13 July 2011

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Lima HSantos CMeiguins B(2021)A Survey of Music Visualization TechniquesACM Computing Surveys10.1145/346183554:7(1-29)Online publication date: 18-Jul-2021
https://dl.acm.org/doi/10.1145/3461835
Zhao NKim NHerman LPfister HLau REchevarria JBylinskii ZBernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)ICONATE: Automatic Compound Icon Generation and IdeationProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376618(1-13)Online publication date: 21-Apr-2020
https://dl.acm.org/doi/10.1145/3313831.3376618
Uno AItoh TCho YShin SKim SHung CHong J(2014)MALLProceedings of the 29th Annual ACM Symposium on Applied Computing10.1145/2554850.2554861(939-944)Online publication date: 24-Mar-2014
https://dl.acm.org/doi/10.1145/2554850.2554861

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Music/lyrics composition system considering user's image and music genre

A voice-to-MIDI system for singing melodies with lyrics

LAMP, A Lyrics and Audio MandoPop Dataset for Music Mood Estimation: Dataset Compilation, System Construction, and Testing

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