Enriching music mood annotation by semantic association reasoning

Mood annotation of music is challenging as it concerns not only audio content but also extra-musical information. It is a representative research topic about how to traverse the well-known semantic gap. In this paper, we propose a new music-mood-specific ontology. Novel ontology-based semantic reasoning methods are applied to effectively bridge content-based information with web-based resources. Also, the system can automatically discover closely relevant semantics for music mood and thus a novel weighting method is proposed for mood propagation. Experiments show that the proposed method outperforms purely content-based methods and significantly enhances the mood prediction accuracy. Furthermore, evaluations show the system´s accuracy could be promisingly increased with the enrichment of metadata.