Emotion-based music retrieval on a well-reduced audio feature space

Music expresses emotion. A number of audio extracted features have influence on the perceived emotional expression of music. These audio features generate a high-dimensional space, on which music similarity retrieval can be performed effectively, with respect to human perception of the music-emotion. However, the real-time systems that retrieve music over large music databases, can achieve order of magnitude performance increase, if applying multidimensional indexing over a dimensionally reduced audio feature space. To meet this performance achievement, in this paper, extensive studies are conducted on a number of dimensionality reduction algorithms, including both classic and novel approaches. The paper clearly envisages which dimensionality reduction techniques on the considered audio feature space, can preserve in average the accuracy of the emotion-based music retrieval.