Polyphonic Music Retrieval with Classifier Ensembles

Abstract Music comparison and retrieval tasks rely on the concept of music similarity, whatever this might be. No similarity measure performs the best for all tasks, genres, or music formats. It is not even easy to formalize human perception of music similarity. A number of papers in the literature deal with the development of appropriate similarity measures. In this paper, we pose the problem under a different perspective, showing that a careful combination of different measures through an ensemble of classifiers performs in a more robust way than any of the particular measures involved, when they are applied as stand-alone measures. The task posed here is the retrieval of a music work from a repository, given a polyphonic score as a query. For the experiments, five state-of-the-art polyphonic similarity measures and three different corpora of polyphonic music scores have been tested.