  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.
