Music Emotion Classification: A Fuzzy Approach

Y.-H. Yang, C.-C. Liu, and H.-H. Chen, "Music emotion classification: A fuzzy approach,"
in Proc. ACM Multimedia 2006 (ACMMM'06).

Due to the subjective nature of human perception, classification of the emotion of music is a challenging problem. Simply assigning an emotion class to a song segment in a deterministic way does not work well because not all people share the same feeling for a song. In this paper, we consider a different approach to music emotion classification. For each music segment, the approach determines how likely the song segment belongs to an emotion class. Two fuzzy classifiers are adopted to provide the measurement of the emotion strength. The measurement is also found useful for tracking the variation of music emotions in a song. Results are shown to illustrate the effectiveness of the approach.


1) Fuzzy k-NN classifier
      A Fuzzy k-Nearest Neighbor Algorithm.
      A Fuzzy K-NN Algorithm Using Weights from the Variance of Membership Values.
2) Fuzzy Nearest-Mean classifier
      Fuzzy Nearest Prototype Classifier Applied to Speaker Identification.


1) We propose a fuzzy emotion classification system that can measure the relative strength of music emotion.
2) This approach performs better than conventional deterministic approaches because it is able to incorporate the subjective nature of emotion perception in the classification.
3) Classification accuracy reaches 78.33% by fuzzy neaerest-mean classifier with Psy15 feature set.
4) A music emotion variation detection scheme to track the variation of emotions in a song.

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