Please use this identifier to cite or link to this item:
|Title:||THREE CURRENT ISSUES IN MUSIC AUTOTAGGING|
Marcos Aurélio Domingues
|Abstract:||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 performanc|
|Appears in Collections:||CTM - Indexed Articles in Conferences|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.