Performances of low-level audio classifiers for large-scale music similarity

This paper proposes a survey of the performances of binary classifiers based on low-level audio features, for music similarity in large-scale databases. Various low-level descriptors are used individually and then combined using several fusion schemes in a content-based audio retrieval system. We show the performances of the classifiers in terms of pruning and loss and we demonstrate that some combination schemes achieve a better performance at a minimum computational cost.