  Deep neural networks (DNN) have been successfully applied to music
classification including music tagging. However, there are several open
questions regarding the training, evaluation, and analysis of DNNs. In this
article, we investigate specific aspects of neural networks, the effects of
noisy labels, to deepen our understanding of their properties. We analyse and
(re-)validate a large music tagging dataset to investigate the reliability of
training and evaluation. Using a trained network, we compute label vector
similarities which is compared to groundtruth similarity.
  The results highlight several important aspects of music tagging and neural
networks. We show that networks can be effective despite relatively large error
rates in groundtruth datasets, while conjecturing that label noise can be the
cause of varying tag-wise performance differences. Lastly, the analysis of our
trained network provides valuable insight into the relationships between music
tags. These results highlight the benefit of using data-driven methods to
address automatic music tagging.
