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Pulling strings from a tangle: visualizing a personal music listening history
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International Conference on Intelligent User Interfaces archive
Proceedings of the 13th international conference on Intelligent user interfaces table of contents
Sanibel Island, Florida, USA
SESSION: Short papers table of contents
Pages 439-444  
Year of Publication: 2009
ISBN:978-1-60558-168-2
Authors
Dominikus Baur  University of Munich, Munich, Germany
Andreas Butz  University of Munich, Munich, Germany
Sponsors
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
ACM: Association for Computing Machinery
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
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ABSTRACT

The history of songs, to which a person has listened, is a very personal piece of information. It is a rich data set that comes as a byproduct of the use of digital music players and can be obtained without interfering with the user.

In this paper, we present three visualizations for this data set and a mechanism for generating new playlists from the user's own listening history, based on a navigation metaphor. First, temporal proximity is interpreted as a simple similarity measure to lay out the entire history on a two-dimensional plane. Closed listening sessions are then used to make chronological relations visible.

The generated playlists mimic the user's previous listening behavior, and the visualizations make the automatic choices understandable, as they share visual properties with the history. In this sense, our visualizations provide a visual vocabulary for listening behaviors and bring scrutability to automatic playlist generation.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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J.-J. Aucouturier and F. Pachet. Improving timbre similarity: How high's the sky? Journal of Negative Results in Speech and Audio Sciences, 1(1), 2004.
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R. Lambiotte and M. Ausloos. Uncovering collective listening habits and music genres in bipartite networks. Physical review, 72(6), Dec. 2005.
 
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E. Pampalk. Islands of music: Analysis, organization, and visualization of music archives. Technical report, Vienna University of Technology, 2001.
 
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E. Pampalk, T. Pohle, and G. Widmer. Dynamic playlist generation based on skipping behaviour. In Proc. of the 6th ISMIR Conference, pages 634--637, 2005.
 
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S. Pauws and B. Eggen. PATS: Realization and user evaluation of an automatic playlist generator. In Proceedings of the Third International Conference on Music Information Retrieval. Paris: IRCAM, pages 222--230, 2002.

Collaborative Colleagues:
Dominikus Baur: colleagues
Andreas Butz: colleagues