Spotivibes: Tagging Playlist Vibes With Colors

Music is often both personally and affectively meaningful to human listeners. However, little work has been done to create music recommender systems that take this into account. In this demo proposal, we present Spotivibes: a first prototype for a new colorbased tagging and music recommender system. This innovative tagging system is designed to take the users’ personal experience of music into account and allows them to tag their favorite songs in a non-intrusive way, which can be generalized to their entire library. The goal of Spotivibes is twofold: to help users better tag their playlists to get better playlists and to provide research data on implicit grouping mechanisms in personal music collections. The system was tested with a user study on 34 Spotify users.

[1]  Martha Larson,et al.  On the Automatic Identification of Music for Common Activities , 2017, ICMR.

[2]  Yi-Hsuan Yang,et al.  Developing a benchmark for emotional analysis of music , 2017, PloS one.

[3]  Martha Larson,et al.  Go with the Flow: When Listeners Use Music as Technology , 2016, ISMIR.

[4]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[5]  Markus Schedl,et al.  The neglected user in music information retrieval research , 2013, Journal of Intelligent Information Systems.

[6]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[7]  Edith Law,et al.  Input-agreement: a new mechanism for collecting data using human computation games , 2009, CHI.

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

[9]  Adrian C. North,et al.  Uses of Music in Everyday Life , 2004 .

[10]  Remco C. Veltkamp,et al.  Studying emotion induced by music through a crowdsourcing game , 2016, Inf. Process. Manag..

[11]  Torsten Möller,et al.  A Survey on Music Listening and Management Behaviours , 2012, ISMIR.

[12]  Joshua D. Reiss,et al.  Music Information Technology and Professional Stakeholder Audiences: Mind the Adoption Gap , 2012, Multimodal Music Processing.

[13]  Jeffrey J. Scott,et al.  State of the Art Report: Music Emotion Recognition: A State of the Art Review , 2010, ISMIR.

[14]  J. Stephen Downie,et al.  Ten Years of ISMIR: Reflections on Challenges and Opportunities , 2009, ISMIR.

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

[16]  Grigorios Tsoumakas,et al.  Multi-Label Classification of Music into Emotions , 2008, ISMIR.