Abstract:
Music is a link between cognition and emotion, and
people are not able to share same feeling for a song.
There has a need to process vast qualities of musical
data. One of the operations is music emotion
classification which is very popular today and an
automatic extraction is needed, relating to various
aspects of music. Music emotion recognition through
a learning model is considered in this paper. In
order to capture the salient nature of music signals
features such as cepstral is applied. Classification of
music signals is considered by Gaussian Mixture
Model (GMM). In this approach, Thayer’s model is
adopted for the description of emotions. This music
mood detection approach is validated through an
experimental study on a dataset containing 60
famous popular songs from English albums.
