Hierarchical Dirichlet Process Mixture Model for Music Emotion Recognition

Jia Ching Wang, Yuan Shan Lee, Yu Hao Chin, Ying Ren Chen, Wen Chi Hsieh

研究成果: 雜誌貢獻期刊論文同行評審

19 引文 斯高帕斯(Scopus)

摘要

This study proposes a novel multi-label music emotion recognition (MER) system. An emotion cannot be defined clearly in the real world because the classes of emotions are usually considered overlapping. Accordingly, this study proposes an MER system that is based on hierarchical Dirichlet process mixture model (HPDMM), whose components can be shared between models of each emotion. Moreover, the HDPMM is improved by adding a discriminant factor to the proposed system based on the concept of linear discriminant analysis. The proposed system represents an emotion using weighting coefficients that are related to a global set of components. Moreover, three methods are proposed to compute the weighting coefficients of testing data, and the weighting coefficients are used to determine whether or not the testing data contain certain emotional content. In the tasks of music emotion annotation and retrieval, experimental results show that the proposed MER system outperforms state-of-the-art systems in terms of F- score and mean average precision.

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文章編號7064768
頁(從 - 到)261-271
頁數11
期刊IEEE Transactions on Affective Computing
6
發行號3
DOIs
出版狀態已出版 - 1 7月 2015

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