Research Seminars

In Search of the Goosebump Factor - A Blueprint for Emotional Music Recommenders

Stephan Baumann
Competence Center Computational Culture (C4), German Research Center for AI in Kaiserslautern (DFKI)

14 November 2007

Abstract

Music Information Retrieval (MIR) as an interdisciplinary research discipline achieved impressive progress over the last decade. Pandora, Last.fm or MyStrands are successful commercial webservices offering previously unavailable convenience for customers. Although such systems compute personalised recommendations based on relevance feedback on top of content-based, expert-based or community metadata, the embedding of emotional context is still a challenge. In my talk I will sketch a blueprint towards an architecture of an emotional music recommender in order to solve the abovementioned problem. The approach is in its infancy but we have already the core ingredients developed. Lifestream aggregation from Web2.0 platforms and the analysis of blog postings will be aligned with the analysis of song lyrics. Furthermore we propose an open Web2.0 platform in order to collect personal descriptions of "goosebump sensations" when listening to music. This collection will be available to researchers in the field to serve as a common ground for training emotional classifiers.

Biography:

Stephan Baumann is heading the Competence Center Computational Culture (C4) at the German Research Center for AI in Kaiserslautern (DFKI) . He is currently engaged in research cooperations working on the cutting-edge in the Social Web. He co-founded the German Hard Blogging Scientists, a multi-author weblog, and is in close cooperation with european startups, german Web2.0 activists and high-potential Ph.D students all over Europe. Prior to this he did a Ph.D on Artificial Listening Systems at DFKI and IRCAM/Paris. In parallel he co-founded the sonicson GmbH – a startup for music recommendation engines - which was sold to Bertelsmann in 2004. His current research interests are in Digital Identity, Social Network Analysis and Visualization, Pervasive Games and Emotional Music Recommenders.