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Mood recognition is an important problem in music informatics and has keyapplications in music discovery and recommendation. These applications havebecome even more relevant with the rise of music streaming. Our workinvestigates the research question of whether we can leverage audio metadatasuch as artist and year, which is readily available, to improve the performanceof mood classification models. To this end, we propose a multi-task learningapproach in which a shared model is simultaneously trained for mood andmetadata prediction tasks with the goal to learn richer representations.Experimentally, we demonstrate that applying our technique on the existingstate-of-the-art convolutional neural networks for mood classification improvestheir performances consistently. We conduct experiments on multiple datasetsand report that our approach can lead to improvements in the average precisionmetric by up to 8.7 points.

Multi-task Learning with Metadata for Music Mood Classification