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Evaluating Mobile Music Experiences: Radio On-the-Go

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


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).


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

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

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    The radio stations are (1) CFRC 101.9 FM, Queens University Radio (, (2) CFUV 101.9 FM, University of Victoria Radio (, (3) KUSF 90.3 FM, University of San Francisco Radio (, (4) CFYT 106.9 FM, Dawson City Community Radio ( and (5) WRHU 88.7 FM, Hofstra University Radio (

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    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.

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    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.

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

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

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    All the audio used in the experiment can be heard at

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


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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.

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Correspondence to Louis Lei Yu .

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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.

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