Comunicació presentada a la 6th Sound and Music Computing Conference, celebrada els dies 23 a 25 de juliol de 2009 a Porto, Portugal.This paper presents an in–depth study of the social tagging
mechanisms used in Freesound.org, an online community
where users share and browse audio files by means of tags
and content–based audio similarity search. We performed
two analyses of the sound collection. The first one is related
with how the users tag the sounds, and we could detect some
well–known problems that occur in collaborative tagging
systems (i.e. polysemy, synonymy, and the scarcity of the
existing annotations). Moreover, we show that more than
10% of the collection were scarcely annotated with only one
or two tags per sound, thus frustrating the retrieval task. In
this sense, the second analysis focuses on enhancing the semantic
annotations of these sounds, by means of content–
based audio similarity (autotagging). In order to “autotag”
the sounds, we use a k–NN classifier that selects the available
tags from the most similar sounds. Human assessment
is performed in order to evaluate the perceived quality of the
candidate tags. The results show that, in 77% of the sounds
used, the annotations have been correctly extended with the
proposed tags derived from audio similarity
