On the Applicability of Peer-to-peer Data in Music Information Retrieval Research.

Peer-to-Peer(p2p)networksarebeingincreasinglyadopted as an invaluable resource for variousmusic informationretrieval (MIR) tasks, including music similarity, recommendation and trend prediction. However, these networks are usually extremely large and noisy, which raises doubts regarding the ability to actually extract sufficiently accurate information. Thispaper evaluatesthe applicabilityof using data originating from p2p networks for MIR research, focusing on partial crawling, inherent noise and localization of songs andsearchqueries. Theseaspectsarequantifiedusingsongs collected from the Gnutella p2p network. We show that the power-law nature of the network makes it relatively easy to capture an accurate view of the main-streams using relatively little effort. However, some applications, like trend prediction, mandate collection of the data from the “long tail”, hence a much more exhaustivecrawl is needed. Furthermore, we present techniques for overcoming noise originating from user generated content and for filtering non informative data, while minimizing information loss.