  Recently, digital music libraries have been developed and can be plainly
accessed. Latest research showed that current organization and retrieval of
music tracks based on album information are inefficient. Moreover, they
demonstrated that people use emotion tags for music tracks in order to search
and retrieve them. In this paper, we discuss separability of a set of emotional
labels, proposed in the categorical emotion expression, using Fisher's
separation theorem. We determine a set of adjectives to tag music parts: happy,
sad, relaxing, exciting, epic and thriller. Temporal, frequency and energy
features have been extracted from the music parts. It could be seen that the
maximum separability within the extracted features occurs between relaxing and
epic music parts. Finally, we have trained a classifier using Support Vector
Machines to automatically recognize and generate emotional labels for a music
part. Accuracy for recognizing each label has been calculated; where the
results show that epic music can be recognized more accurately (77.4%),
comparing to the other types of music.
