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

Characterization and exploitation of community structure in cover song networks