Unsupervised Accuracy Improvement for Cover Song Detection Using Spectral Connectivity Network

This paper introduces a new method for improving the accuracy in medium scale music similarity problems. Recently, it has been shown that the raw accuracy of query by example systems can be enhanced by considering priors about the distribution of its output or the structure of the music collection being considered. The proposed approach focuses on reducing the dependency to those priors by considering an eigenvalue decomposition of the aforementioned system’s output. Experiments carried out in the framework of cover song detection show that the proposed approach has good performance for enhancing a high accuracy system. Furthermore, it maintains the accuracy level for lower performing systems.