Skip to main content

Evaluating Mobile Music Experiences: Radio On-the-Go

  • 744 Accesses

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 240)

Abstract

Music has become an accompaniment to everyday activities, such as shopping and navigating. Although people listen to music in a context-driven manner, music recommendation services typically ignore where a user is listening to the music. They also typically select music based on a single seed song, rather than ordering a user’s created playlists for the best user experience. The contributions of this paper are three-fold: (1) We present a survey of 15 DJs of college radio stations to identify their heuristics in creating playlists for radio shows. (2) We present an experimental study design to evaluate various scheduling (track ordering) strategies for mobile music consumption in situ, which is used to (3) conduct a field experiment that compares the user experience of three scheduling strategies (tempo, genre and location) against the gold standard of a playlist created by an experienced DJ (This work was completed when Anupriya Ankolekar and Thomas Sandholm were both researchers, and Louis Lei Yu was a postdoctoral research fellow at Hewlett Packard Labs. The majority of the experiments were conducted during the summer of 2011. The authors are listed here in alphabetical order).

Keywords

  • User experience
  • Mobile music consumption
  • Music scheduling
  • Experiment design

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-90740-6_4
  • Chapter length: 18 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   54.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-90740-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   72.00
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.

Notes

  1. 1.

    pandora.com.

  2. 2.

    last.fm.

  3. 3.

    spotify.com.

  4. 4.

    echonest.com.

  5. 5.

    In this context, meaning the order in which we choose to play songs.

  6. 6.

    soundtracker.fm.

  7. 7.

    soundtracking.com.

  8. 8.

    rjdj.me.

  9. 9.

    The radio stations are (1) CFRC 101.9 FM, Queens University Radio (http://cfrc.ca), (2) CFUV 101.9 FM, University of Victoria Radio (http://cfuv.uvic.ca), (3) KUSF 90.3 FM, University of San Francisco Radio (http://savekusf.org), (4) CFYT 106.9 FM, Dawson City Community Radio (http://cfyt.ca) and (5) WRHU 88.7 FM, Hofstra University Radio (http://www.hofstra.edu/Academics/Colleges/SOC/WRHU).

  10. 10.

    Unlike commercial radio stations whose playlists have been automatically generated [27] to get the best possible ratings [12], college radio stations still tend to have DJs who choose and schedule the music for their own shows.

  11. 11.

    http://allmusic.com.

  12. 12.

    RMS amplitude extracted using the sox tool (sox audiofile.wav stats | grep “RMS amplitude” | awk {‘print $3’}). We got the same ordering results when extracting loudness using the RMS lev dB feature. We also found that pitch extraction did not produce any useful schedules so we dropped it.

  13. 13.

    Number of beats detected by the aubiocut tool (aubiocut -b -i audiofile.wav | wc -l).

  14. 14.

    In our system we normalize this to 1 min for all songs.

  15. 15.

    All the audio used in the experiment can be heard at http://www.crowdee.com/dj.

  16. 16.

    In fact, there are several applications that do this already, e.g. SynchStep (synchstep.com) and TrailMix (trailmixapp.com).

References

  1. Agarwal, A., Meyer, A.: Beyond usability: evaluating emotional response as an integral part of the user experience. In: Proceedings of CHI 2009, pp. 2919–2930. ACM (2009)

    Google Scholar 

  2. Alghoniemy, M., Tewfik, A.H.: User-defined music sequence retrieval. In: Proceedings of the Eighth ACM International Conference on Multimedia, Multimedia 2000, pp. 356–358. ACM, New York (2000)

    Google Scholar 

  3. Ankolekar, A., Sandholm, T., Yu, L.: Play it by ear: a case for serendipitous discovery of places with musicons. In: Proceedings of CHI 2013, pp. 2959–2968 (2013)

    Google Scholar 

  4. Avesani, P., Massa, P., Nori, M., Susi, A.: Collaborative radio community. In: De Bra, P., Brusilovsky, P., Conejo, R. (eds.) AH 2002. LNCS, vol. 2347, pp. 462–465. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47952-X_61

    CrossRef  Google Scholar 

  5. Bosteels, K., Pampalk, E., Kerre, E.E.: Evaluating and analysing dynamic playlist generation heuristics using radio logs and fuzzy set theory. In: Proceedings of ISMIR (2009)

    Google Scholar 

  6. Bull, M.: Sounding Out the City: Personal Stereos and the Management of Everyday Life. Berg, Oxford (2000)

    Google Scholar 

  7. Bull, M.: Sound Moves: iPod Culture and Urban Experience. Routledge, Abingdon (2008)

    Google Scholar 

  8. CFRC 101.9 FM: CFRC Volunteer Manual, December 2010. http://cfrc.ca/blog/wp-content/uploads/2009/03/volunteer-manual-32.pdf

  9. CFUV 101.9 FM: CFUV Orientation Guide, November 2009. http://cfuv.uvic.ca/cms/wp-content/uploads/2012/03/Orientation-Manual-09-10-6th-edition-1.pdf

  10. CJSR 88.5 FM: Music Show Basics, December 2001. http://www.firststage.ca/csirp/training/articles/musicshowbasics.html

  11. Denora, T.: Music in Everyday Life. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  12. Eastman, S., Ferguson, D.: Media Programming: Strategies and Practices. Thomson/Wadsworth, Belmont (2008)

    Google Scholar 

  13. Gaye, L., Holmquist, L.E., Behrendt, F., Tanaka, A.: Mobile music technology: report on an emerging community. In: Proceedings of the 2006 Conference on New Interfaces for Musical Expression, NIME 2006, pp. 22–25 (2006)

    Google Scholar 

  14. Goodman, J., Brewster, S.A., Gray, P.: How can we best use landmarks to support older people in navigation? J. Behav. Inf. Technol. 24, 3–20 (2005)

    CrossRef  Google Scholar 

  15. Goodman, J., Brewster, S., Gray, P.: Using field experiments to evaluate mobile guides. In: Proceedings of HCI in Mobile Guides, Workshop at Mobile HCI 2004 (2004)

    Google Scholar 

  16. Hauver, D., French, J.: Flycasting: using collaborative filtering to generate a playlist for online radio. In: 2001 Proceedings of First International Conference on Web Delivering of Music, pp. 123–130, 23–24 November 2001

    Google Scholar 

  17. Hayes, C., Cunningham, P.: Smart radio: building music radio on the fly. In: Expert Systems, vol. 2000, pp. 2–6. ACM Press (2000)

    Google Scholar 

  18. Komulainen, S., Karukka, M., Häkkilä, J.: Social music services in teenage life: a case study. In: Proceedings of the 22nd Conference of the Computer-Human Interaction Special Interest Group of Australia on Computer-Human Interaction, OZCHI 2010, pp. 364–367 (2010)

    Google Scholar 

  19. Logan, B.: Content-based playlist generation: exploratory experiments. In: Proceedings of 3rd International Conference on Music Information Retrieval, Paris, France (2002)

    Google Scholar 

  20. Mehrabian, A., Russell, J.A.: An Approach to Environmental Psychology. M.I.T. Press, Cambridge (1974)

    Google Scholar 

  21. Nemirovsky, P., Davenport, G.: Guideshoes: navigation based on musical patterns. In: CHI 1999 Extended Abstracts on Human Factors in Computing Systems, CHI EA 1999, pp. 266–267 (1999)

    Google Scholar 

  22. Nettamo, E., Nirhamo, M., Häkkilä, J.: A cross-cultural study of mobile music - retrieval, management and consumptiom. In: OZCHI 2006, pp. 87–94. ACM (2006)

    Google Scholar 

  23. Pampalk, E., Pohle, T., Widmer, G.: Dynamic playlist generation based on skipping behavior. In: Proceedings of ISMIR (2005)

    Google Scholar 

  24. Pauws, S., Eggen, B.: PATS: realization and user evaluation of an automatic playlist generator. In: ISMIR, pp. 222–230 (2002)

    Google Scholar 

  25. Pauws, S., Verhaegh, W., Vossen, M.: Music playlist generation by adapted simulated annealing. Inf. Sci. 178(3), 647–662 (2008)

    CrossRef  Google Scholar 

  26. Ragno, R., Burges, C.J.C., Herley, C.: Inferring similarity between music objects with application to playlist generation. In: Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2005, pp. 73–80. ACM, New York (2005)

    Google Scholar 

  27. Surhone, L., Tennoe, M., Henssonow, S.: Radio Computing Services

    Google Scholar 

  28. Warren, N., Jones, M., Jones, S., Bainbridge, D.: Navigation via continuously adapted music. In: CHI EA 2005, pp. 1849–1852 (2005)

    Google Scholar 

Download references

Acknowledgments

This work was completed during the authors’ time at Hewlett Packard Labs. The authors would like to thank Bernardo Huberman, senior HP fellow, for his guidance.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Louis Lei Yu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Ankolekar, A., Sandholm, T., Yu, L.L. (2018). Evaluating Mobile Music Experiences: Radio On-the-Go. In: Murao, K., Ohmura, R., Inoue, S., Gotoh, Y. (eds) Mobile Computing, Applications, and Services. MobiCASE 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 240. Springer, Cham. https://doi.org/10.1007/978-3-319-90740-6_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-90740-6_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90739-0

  • Online ISBN: 978-3-319-90740-6

  • eBook Packages: Computer ScienceComputer Science (R0)