Music emotion classification: a fuzzy approach

ABSTRACT

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.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.5 Sound and Music Computing
          Subjects: Systems

General Terms:
Algorithms, Experimentation, Human Factors, Performance

Keywords:
emotion classifier (EC), fuzzy k-NN (FKNN), fuzzy nearest-mean (FNM), fuzzy vector, model generator (MG), music emotion strength, music emotion variation detection (MEVD)