  Mood recognition is an important problem in music informatics and has key
applications in music discovery and recommendation. These applications have
become even more relevant with the rise of music streaming. Our work
investigates the research question of whether we can leverage audio metadata
such as artist and year, which is readily available, to improve the performance
of mood classification models. To this end, we propose a multi-task learning
approach in which a shared model is simultaneously trained for mood and
metadata prediction tasks with the goal to learn richer representations.
Experimentally, we demonstrate that applying our technique on the existing
state-of-the-art convolutional neural networks for mood classification improves
their performances consistently. We conduct experiments on multiple datasets
and report that our approach can lead to improvements in the average precision
metric by up to 8.7 points.
