1. Thomas Lidy, Rudolf Mayer, Andy Rauber, Pedro J. Ponce de León, Antonio Pertusa, José M. Iñesta

, vol. Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010), pp. 279-284. Utrecht, Netherlands (2010)

We present a cartesian ensemble classification system that is based on the principle of late fusion and feature subspaces. These feature subspaces describe different aspects of the same data set. The framework is built on the Weka machine learning toolkit and able to combine arbitrary feature sets and learning schemes. In our scenario, we use it for the ensemble classification of multiple feature sets from the audio and symbolic domains. We present an extensive set of experiments in the context of music genre classification, based on numerous Music IR benchmark datasets, and evaluate a set of combination/voting rules. The results show that the approach is superior to the best choice of a single algorithm on a single feature set. Moreover, it also releases the user from making this choice explicitly.