Emotion Recognition From Music Enhanced By Domain Knowledge
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Music elements have been widely used to influence the audiences’ emotional experience by its music grammar. However, these domain knowledge, has not been thoroughly explored as music grammar for music emotion analyses in previous work. In this paper, we propose a novel method to analyze music emotion via utilizing the domain knowledge of music elements. Specifically, we first summarize the domain knowledge of music elements and infer probabilistic dependencies between different main musical elements and emotions from the summarized music theory. Then, we transfer the domain knowledge to constraints, and formulate affective music analysis as a constrained optimization problem. Experimental results on the Music in 2015 database and the AMG1608 database demonstrate that the proposed music content analyses method outperforms the state-of-the-art performance prediction methods.
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