  In this paper, we analyze web-downloaded data on people sharing their music
library, that we use as their individual musical signatures (IMS). The system
is represented by a bipartite network, nodes being the music groups and the
listeners. Music groups audience size behaves like a power law, but the
individual music library size is an exponential with deviations at small
values. In order to extract structures from the network, we focus on
correlation matrices, that we filter by removing the least correlated links.
This percolation idea-based method reveals the emergence of social communities
and music genres, that are visualised by a branching representation. Evidence
of collective listening habits that do not fit the neat usual genres defined by
the music industry indicates an alternative way of classifying listeners/music
groups. The structure of the network is also studied by a more refined method,
based upon a random walk exploration of its properties. Finally, a personal
identification - community imitation model (PICI) for growing bipartite
networks is outlined, following Potts ingredients. Simulation results do
reproduce quite well the empirical data.
