  The use of community detection algorithms is explored within the framework of
cover song identification, i.e. the automatic detection of different audio
renditions of the same underlying musical piece. Until now, this task has been
posed as a typical query-by-example task, where one submits a query song and
the system retrieves a list of possible matches ranked by their similarity to
the query. In this work, we propose a new approach which uses song communities
to provide more relevant answers to a given query. Starting from the output of
a state-of-the-art system, songs are embedded in a complex weighted network
whose links represent similarity (related musical content). Communities inside
the network are then recognized as groups of covers and this information is
used to enhance the results of the system. In particular, we show that this
approach increases both the coherence and the accuracy of the system.
Furthermore, we provide insight into the internal organization of individual
cover song communities, showing that there is a tendency for the original song
to be central within the community. We postulate that the methods and results
presented here could be relevant to other query-by-example tasks.
