Extending the folksonomies of freesound.org using content-based audio analysis

Abstract

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.