Song Emotion Recognition: a Performance Comparison Between Audio Features and Artificial Neural Networks
This work addresses emotion recognition in music for applications like music recommendation, but it is incremental as it focuses on comparing existing methods.
The paper compared common audio features and models for recognizing emotion in a cappella songs, identifying the best-suited ones for this task.
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 perception 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 to predict what is the probability that a given song matches 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 recognizing emotion in a cappella songs.