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nepDroid: an intelligent mobile music player

Published: 05 June 2012 Publication History

Abstract

Mobile music consumption has been spiraling during the past couple of years. The interaction techniques provided to sift through the ever increasing amounts of music available on smart devices unfortunately have not. In this paper, we address this issue and present an intelligent mobile user interface that enables the user to browse her mobile music collection in a joyful and informed way.

References

[1]
J.-J. Aucouturier and F. Pachet. Improving Timbre Similarity: How High is the Sky? Journal of Negative Results in Speech and Audio Sciences, 1(1), 2004.
[2]
P. Knees, M. Schedl, T. Pohle, and G. Widmer. An Innovative Three-Dimensional User Interface for Exploring Music Collections Enriched with Meta-Information from the Web. In Proc. ACM Multimedia, Santa Barbara, CA, USA, Oct 2006.
[3]
P. Knees, M. Schedl, T. Pohle, and G. Widmer. Exploring Music Collections in Virtual Landscapes. IEEE MultiMedia, 14(3):46--54, Jul--Sep 2007.
[4]
T. Kohonen. Self-Organizing Maps, volume 30 of Springer Series in Information Sciences. Springer, Berlin, Germany, 3rd edition, 2001.
[5]
E. Pampalk, S. Dixon, and G. Widmer. On the Evaluation of Perceptual Similarity Measures for Music. In Proc. DAFx, London, UK, Sep 2003.
[6]
E. Pampalk, S. Dixon, and G. Widmer. Exploring Music Collections by Browsing Different Views. Computer Music Journal, 28(3), 2004.
[7]
M. Schedl, C. Höglinger, and P. Knees. Large-Scale Music Exploration in Hierarchically Organized Landscapes Using Prototypicality Information. In Proc. ACM ICMR, Trento, Italy, Apr 2011.

Cited By

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  • (2017)Intelligent User Interfaces for Social Music Discovery and Exploration of Large-scale Music RepositoriesProceedings of the 2017 ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces10.1145/3039677.3039678(7-11)Online publication date: 13-Mar-2017

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  1. nepDroid: an intelligent mobile music player

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    George Popescu

    This short paper presents a novel interaction technique for smartphones that allows users to browse music stored on mobile devices in a game-like way. Unlike music retrieval methods based on metadata text search, the nepDroid mobile music player uses clustering and visualization techniques to display relevant music information. The system positions a small Droid icon at the center of the interface, which shows music collections retrieved from the mobile phone's memory in a color landscape format. "Island" visualizations represent clusters of similar types of music, such as hard rock. Notably, audio features such as mel-frequency cepstral coefficients (MFCCs), which "describe timbre," and fluctuation patterns (FPs), which "reflect rhythmic properties," are extracted, and a clustering algorithm is used to display the distribution of music items in a 3D space. Clusters are visualized via a smoothed data histogram (SDH), and high values of music density are mapped to color values. When the main icon navigates close to a song, nepDroid automatically plays it. From the user experience point of view, this represents a very promising approach to music navigation and visualization. Due to the high complexity of the system, the visualization of a library of a few hundred songs requires high computational hardware resources, which are limited even on latest-generation mobile phones. However, the system is a viable approach for small-scale computation and visualization. Online Computing Reviews Service

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    Published In

    cover image ACM Conferences
    ICMR '12: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
    June 2012
    489 pages
    ISBN:9781450313292
    DOI:10.1145/2324796
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 05 June 2012

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    Author Tags

    1. clustering
    2. content-based music similarity
    3. music information retrieval
    4. user interface
    5. visualization

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    ICMR '12 Paper Acceptance Rate 50 of 145 submissions, 34%;
    Overall Acceptance Rate 254 of 830 submissions, 31%

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    • (2017)Intelligent User Interfaces for Social Music Discovery and Exploration of Large-scale Music RepositoriesProceedings of the 2017 ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces10.1145/3039677.3039678(7-11)Online publication date: 13-Mar-2017

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