  Music Genres serve as an important meta-data in the field of music
information retrieval and have been widely used for music classification and
analysis tasks. Visualizing these music genres can thus be helpful for music
exploration, archival and recommendation. Probabilistic topic models have been
very successful in modelling text documents. In this work, we visualize music
genres using a probabilistic topic model. Unlike text documents, audio is
continuous and needs to be sliced into smaller segments. We use simple MFCC
features of these segments as musical words. We apply the topic model on the
corpus and subsequently use the genre annotations of the data to interpret and
visualize the latent space.
