TheMusical Avatar

Abstract

The music we like (i.e. our musical preferences) encodes and communicates key information about ourselves. Depicting such preferences in a condensed and easily understandable way is very appealing, especially considering the current trends in social network communication. In this paper we propose a method to automatically generate, given a provided set of preferred music tracks, an iconic representation of a user's musical preferences -- the Musical Avatar. Starting from the raw audio signal we first compute over 60 low-level audio features. Then, by applying pattern recognition methods, we infer a set of semantic descriptors for each track in the collection. Next, we summarize these track-level semantic descriptors, obtaining a user profile. Finally, we map this collection-wise description to the visual domain by creating a humanoid cartoony character that represents the user's musical preferences. We performed a proof-of-concept evaluation of the proposed method on 11 subjects with promising results. The analysis of the users' evaluations shows a clear preference for avatars generated by the proposed semantic descriptors over avatars derived from neutral or randomly generated values. We also found a general agreement on the representativeness of the users' musical preferences via the proposed visualization strategy.