Automatic taggers describe music in terms of a multinomial distribution over relevant semantic concepts. This paper presents a framework for improving automatic tagging of music content by modeling contextual relationships between these semantic concepts. The framework extends existing auto-tagging methods by adding a Dirichlet mixture to model the contextual co-occurrences between semantic multinomials. Experimental results show that adding context improves automatic annotation and retrieval of music and demonstrate that the Dirichlet mixture is an appropriate model for capturing co-occurrences between semantics.
['Riccardo Miotto', 'Luke Barrington', 'Gert R. G. Lanckriet']
Improving Auto-tagging by Modeling Semantic Co-occurrences.