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 ...
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.
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