  Audio-based cover song detection has received much attention in the MIR
community in the recent years. To date, the most popular formulation of the
problem has been to compare the audio signals of two tracks and to make a
binary decision based on this information only. However, leveraging additional
signals might be key if one wants to solve the problem at an industrial scale.
In this paper, we introduce an ensemble-based method that approaches the
problem from a many-to-many perspective. Instead of considering pairs of tracks
in isolation, we consider larger sets of potential versions for a given
composition, and create and exploit the graph of relationships between these
tracks. We show that this can result in a significant improvement in
performance, in particular when the number of existing versions of a given
composition is large.
