Abstract
Music can express the emotion of the composer, and influence the emotion of the listener. Emotion expressed in music can be considered as an important characteristic of a song. Automatic music emotion classification could have significant potential in music information retrieval. This paper proposes a hierarchical classification to extract emotional features of classical and pop music, and compares recognition rates through lyrical and music content analysis in three classification methods: K-Nearest Neighbor Rule (KNNR), Gaussian Mixture Model (GMM) and Support Vector Machine (SVM). Results show music from different era will have different emotional features, and KNNR has the highest recognition results . We also use Fuzzy KNNR to compare with the crisp version. In Fuzzy KNNR Algorithm, we generalize our answer between zero and one to make our system more flexible. We also indicate that lyrics are important in the music emotion recognition of pop songs. Though there is still much work to be done in the classification through combining lyrics and music content, it presents a significant improvement over the use of music content alone, and can be considered as a new recognition method