Feature Mapping and Fusion for Music Genre Classification

We propose a feature level fusion that is based on mapping the original low-level audio features to histogram descriptors. Our mapping is based on possibilistic membership functions and has two main components. The first one consists of clustering each set of features and identifying a set of representative prototypes. The second component uses the learned prototypes within membership functions to transform the original features into histograms. The mapping transforms features of different dimensions to histograms of fixed dimensions. This makes the fusion of multiple features less biased by the dimensionality and distributions of the different features. Using a standard collection of songs, we show that the transformed features provide higher classification accuracy than the original features. We also show that mapping simple low-level features and using a K-NN classifier provides results comparable to the state-of-the art.