Three Current Issues In Music Autotagging

The purpose of this paper is to address several aspects of music autotagging. We start by presenting autotagging experiments conducted with two different systems and show performances on a par with a method representative of the state-of-the-art. Beyond that, we illustrate via systematic experiments the importance of a number of issues relevant to autotagging, yet seldom reported in the literature. First, we show that the evaluation of autotagging techniques is fragile in the sense that small alterations to the set of tags to be learned, or in the set of music pieces may lead to dramatically different results. Hence we stress a set of methodological recommendations regarding data and evaluation metrics. Second, we conduct experiments on the generality of autotagging models, showing that a number of different methods at a similar performance level to the state-of-the-art fail to learn tag models able to generalize to datasets from different origins. Third we show that current performance level of a direct mapping between audio features and tags still appears insufficient to enable the possibility of exploiting natural tag correlations as a second stage to improve performance.