A Probabilistic Approach to Merge Context and Content Information for Music Retrieval

An interesting problem in music information retrieval is how to combine the information from different sources in order to improve retrieval effectiveness. This paper introduces an approach to represent a collection of tagged songs through an hidden Markov model with the purpose to develop a system that merges in the same framework both acoustic similarity and semantic descriptions. The former provides content-based information on song similarity, the latter provides context-aware information about individual songs. Experimental results show how the proposed model leads to better performances than approaches that rank songs using both a single information source and a their linear combination.