Unsupervised Detection of Cover Song Sets: Accuracy Improvement and Original Identification

The task of identifying cover songs has formerly been studied in terms of a prototypical query retrieval framework. However, this framework is not the only one the task allows. In this article, we revise the task of identifying cover songs to include the notion of sets (or groups) of covers. In particular, we study the application of unsupervised clustering and community detection algorithms to detect cover sets. We consider current state-of-the-art algorithms and propose new methods to achieve this goal. Our experiments show that the detection of cover sets is feasible, that it can be performed in a reasonable amount of time, that it does not require extensive parameter tuning, and that it presents certain robustness to inaccurate measurements. Furthermore, we highlight two direct outcomes that naturally arise from the proposed framework revision: increasing the accuracy of query retrieval-based systems and detecting the original song within a set of covers.