Design and evaluation of mood pictures in social music discovery service

This paper describes the iterative design process and evaluation of mood pictures in a social music discovery service. The service enables users to consume and collaboratively create playlists based on the pictures. In total, 45 Finnish users took part in the qualitative evaluations. This paper presents the results regarding the preset mood picture design and introduces user-generated mood picture playlists. Based on the results, a set of design implications for mood pictures is introduced. In addition, the consistency of user responses from a quantitative picture–music association test shows the applicability of mood picture–music associations to a music discovery service.

[1]  Stefanie Nowak,et al.  Content-based mood classification for photos and music: a generic multi-modal classification framework and evaluation approach , 2008, MIR '08.

[2]  Marc Leman,et al.  Content-Based Music Information Retrieval: Current Directions and Future Challenges , 2008, Proceedings of the IEEE.

[3]  Nicholas J. Belkin,et al.  Categories of Music Description and Search Terms and Phrases Used by Non-Music Experts , 2002, ISMIR.

[4]  Jukka Holm,et al.  Associating Avatars with Musical Genres , 2010, 2010 14th International Conference Information Visualisation.

[5]  Martin Bichler,et al.  Design science in information systems research , 2006, Wirtschaftsinf..

[6]  David W. McDonald,et al.  Musical fingerprints: collaboration around home media collections , 2009, GROUP '09.

[7]  Mark W. Newman,et al.  Listening in: practices surrounding iTunes music sharing , 2005, CHI.

[8]  Youngmoo E. Kim,et al.  MoodSwings: A Collaborative Game for Music Mood Label Collection , 2008, ISMIR.

[9]  D. Rennie.  Grounded Theory Methodology , 1998 .

[10]  Matt Jones,et al.  Mobile Interaction Design , 2006 .

[11]  Kathy Charmaz,et al.  Grounded Theory: Methodology and Theory Construction , 2001 .

[12]  Fabio Vignoli,et al.  Digital Music Interaction Concepts: A User Study , 2004, ISMIR.

[13]  Jukka Holm,et al.  Associating Emoticons with Musical Genres , 2010, NIME.

[14]  Sally Jo Cunningham,et al.  Finding New Music: A Diary Study of Everyday Encounters with Novel Songs , 2007, ISMIR.

[15]  Masataka Goto,et al.  Musicream: New Music Playback Interface for Streaming, Sticking, Sorting, and Recalling Musical Pieces , 2005, ISMIR.

[16]  Robert G. Capra,et al.  Understanding personal digital music collections , 2010, ASIST.

[17]  Abigail Sellen,et al.  The Use of Conventional and New Music Media: Implications for Future Technologies , 2001, INTERACT.

[18]  T. Eerola.  Are the Emotions Expressed in Music Genre-specific? An Audio-based Evaluation of Datasets Spanning Classical, Film, Pop and Mixed Genres , 2011 .

[19]  Jeffrey J. Scott,et al.  MUSIC EMOTION RECOGNITION: A STATE OF THE ART REVIEW , 2010 .

[20]  Carles Fernandes Julià,et al.  SongExplorer: A Tabletop Application for Exploring Large Collections of Songs , 2009, ISMIR.

[21]  Xiao Hu.  Combining Text and Audio for Mood Classification in Music Digital Libraries , 2009, Bull. IEEE Tech. Comm. Digit. Libr..

[22]  Jarno Ojala,et al.  Evaluating MoodPic - a Concept for Collaborative Mood Music Playlist Creation , 2013, 2013 17th International Conference on Information Visualisation.

[23]  Jukka Holm,et al.  Evaluating an avatar-based user interface for discovering new music , 2010, MUM.

[24]  Anita Shen Lillie,et al.  MusicBox : navigating the space of your music , 2008 .

[25]  S. Gosling,et al.  PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES The Do Re Mi’s of Everyday Life: The Structure and Personality Correlates of Music Preferences , 2003 .

[26]  G. A. Miller.  THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[27]  Frank Bentley,et al.  Personal vs. commercial content: the similarities between consumer use of photos and music , 2006, CHI.

[28]  Simulation Forum,et al.  Proceedings of the 12th IASTED International Conference on Internet and Multimedia Systems and Applications, August 18-20, 2008, Kailua-Kona, Hawaii, USA , 2008 .

[29]  Steve Benford,et al.  Digging in the crates: an ethnographic study of DJS' work , 2012, CHI.

[30]  Jukka Holm,et al.  Using Animated Mood Pictures in Music Recommendation , 2012, 2012 16th International Conference on Information Visualisation.

[31]  J. Stephen Downie,et al.  Improving mood classification in music digital libraries by combining lyrics and audio , 2010, JCDL '10.

[32]  Ichiro Fujinaga,et al.  Musical genre classification: Is it worth pursuing and how can it be improved? , 2006, ISMIR.

[33]  J. Sloboda,et al.  Music and emotion: Theory and research , 2001 .

[34]  Nina Reeves,et al.  An ethnographic study of music information seeking: implications for the design of a music digital library , 2003, 2003 Joint Conference on Digital Libraries, 2003. Proceedings..

[35]  In-Kwon Lee,et al.  Generating affective music icons in the emotion plane , 2009, CHI Extended Abstracts.

[36]  Jukka Holm,et al.  Associating Colours with Musical Genres , 2009 .