Content Management for the Live Music Industry in Virtual Worlds: Challenges and Opportunities

International audienceThe real-world music industry is undergoing a transition away from the retailing and distribution of fixed objects (records, files) to the consumption of live,interactive events (concerts, happenings). This development is paralleled with the recent flourishing of live music in virtual worlds, which in many ways could become the epitome of its real-world counterpart: for the artists, virtual concerts are cheap and easy to organize, and can therefore be a viable alternative to performing in the real world; for the music promoter and marketer, virtual concert attendance can be traced and analyzed more easily than in the real world; for the virtual concertgoer, attending concerts that are happening a (virtual) world away is possible with a single click. Taking insights from both a survey among the Second-Life music practitioners and from our own prototype of a live music recommendation system built on top of Second-Life, this article shows that the technical infrastructure of current virtual worlds is not well-suited to the development of the content management tools needed to support this opportunity. We propose several new ways to address these problems, and advocate for their recognition both by the artistic and the technical community

Combining Metadata, Inferred Similarity of Content, and Human Interpretation for Managing and Listening to Music Collections

Music services, media players and managers provide support for content classification and access based on filtering metadata values, statistics of access and user ratings. This approach fails to capture characteristics of mood and personal history that are often the deciding factors when creating personal playlists and collections in music. This dissertation work presents MusicWiz, a music management environment that combines traditional metadata with spatial hypertext-based expression and automatically extracted characteristics of music to generate personalized associations among songs. MusicWiz’s similarity inference engine combines the personal expression in the workspace with assessments of similarity based on the artists, other metadata, lyrics and the audio signal to make suggestions and to generate playlists. An evaluation of MusicWiz with and without the workspace and suggestion capabilities showed significant differences for organizing and playlist creation tasks. The workspace features were more valuable for organizing tasks, while the suggestion features had more value for playlist creation activities