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Tune in to your emotions: a robust personalized affective music player

Abstract

The emotional power of music is exploited in a personalized affective music player (AMP) that selects music for mood enhancement. A biosignal approach is used to measure listeners’ personal emotional reactions to their own music as input for affective user models. Regression and kernel density estimation are applied to model the physiological changes the music elicits. Using these models, personalized music selections based on an affective goal state can be made. The AMP was validated in real-world trials over the course of several weeks. Results show that our models can cope with noisy situations and handle large inter-individual differences in the music domain. The AMP augments music listening where its techniques enable automated affect guidance. Our approach provides valuable insights for affective computing and user modeling, for which the AMP is a suitable carrier application.

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Acknowledgments

We gratefully acknowledge Marjolein van der Zwaag, Tim Tijs, Kathryn Segovia, and Maurits Kaptein for their helpful comments and vivid discussions on an earlier draft of this paper. We also thank three anonymous reviewers and the editor who all provided us detailed feedback on two earlier versions of this paper. Thanks to their comments and suggestions we have been able to revise this article substantially. Finally, we gratefully acknowledge Lynn Packwood for her careful proof reading.

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Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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Janssen, J.H., van den Broek, E.L. & Westerink, J.H.D.M. Tune in to your emotions: a robust personalized affective music player. User Model User-Adap Inter 22, 255–279 (2012). https://doi.org/10.1007/s11257-011-9107-7

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Keywords

  • Mood
  • Music
  • Psychophysiology
  • User modeling
  • Kernel density estimation
  • Validation
  • Affective computing