Song Emotion Recognition: a Performance Comparison Between Audio Features and Artificial Neural Networks

When songs are composed or performed, there is often an intent by the singer/songwriter of expressing feelings or emotions through it. For humans, matching the emotiveness in a musical composition or performance with the subjective perceptiveness of an audience can be quite challenging. Fortunately, the machine learning approach for this problem is simpler. Usually, it takes a data-set, from which audio features are extracted to present this information to a data-driven model, that will, in turn, train predicting the highest probability of an input song matching a target emotion. In this paper†, we studied the most common features and models used in recent publications to tackle this problem, revealing which ones are best suited for songs a cappella.