Abstract
More and more music is being made available to the music listener today, while people have their favorite music on their mobile players. In this paper, we investigate an approach to automatically updating the music on the mobile player based on personal listening behavior. The aim is to automatically discard those pieces of music from the player the listener is fed up with, while new music is automatically selected from a large amount of available music. The source of new music could be a flat rate music delivery service, where the user pays a monthly fee to have access to a large amount of music. We assume a scenario where only a “skip” button is available to the user, which she presses when the currently playing track does not please her. We evaluate several algorithms and show that the best ones clearly outperform those with lower performance, while it remains open how much they can be improved further.
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References
Aucouturier, J.-J., Pachet, F.: Improving timbre similarity: How high is the sky? Journal of Negative Results in Speech and Audio Sciences 1(1) (2004)
Cunningham, S.J., Downie, J.S., Bainbridge, D.: ”The Pain, the Pain”: Modelling Music Information Behavior and the Songs we Hate. In: Proceedings of the 6th International Conference on Music Information Retrieval, ISMIR 2005 (2005)
Logan, B.: Music Recommendation From Song Sets. In: Proceedings of the 5th International Conference on Music Information Retrieval, ISMIR 2004 (2004)
Mandel, M., Ellis, D.: Song-Level Features and Support Vector Machines for Music Classification. In: Proceedings of the 6th International Conference on Music Information Retrieval, ISMIR 2005 (2005)
Pachet, F., Aucouturier, J.-J., La Burthe, A., Zils, A., Beurive, A.: The cuidado music browser: An end-to-end electronic music distribution system. Multimedia Tools and Applications 33(1), 331–349 (2006); Special Issue on the CBMI 2003 Conference
Pampalk, E.: Computational Models of Music Similarity and their Application in Music Information Retrieval. Docteral dissertation, Vienna University of Technology, Austria (March 2006)
Pampalk, E., Pohle, T., Widmer, G.: Dynamic Playlist Generation Based on Skipping Behaviour. In: Proceedings of the 6th International Conference on Music Information Retrieval, ISMIR 2005 (2005)
Vignoli, F., Pauws, S.: A Music Retrieval System Based on User-Driven Similarity and its Evaluation. In: Proceedings of the 6th International Conference on Music Information Retrieval, ISMIR 2005 (2005)
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Pohle, T., Seyerlehner, K., Widmer, G. (2010). An Approach to Automatically Tracking Music Preference on Mobile Players. In: Detyniecki, M., Leiner, U., Nürnberger, A. (eds) Adaptive Multimedia Retrieval. Identifying, Summarizing, and Recommending Image and Music. AMR 2008. Lecture Notes in Computer Science, vol 5811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14758-6_6
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DOI: https://doi.org/10.1007/978-3-642-14758-6_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14757-9
Online ISBN: 978-3-642-14758-6
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