Using Fisher Kernels and Hidden Markov Models for the Identification of Famous Composers from their Sheet Music

We present a novel application of Fisher kernels to the problem of identifying famous composers from their sheet music. The characteristics of the composers writing style are obtained from note changes on a basic beat level, combined with the notes hidden harmony. We are able to extract this information by the application of a Hidden Markov Model to learn the underlying probabilistic structure of the score. We are able to use the model to generate new sheet music based on a specific composer. Furthermore we do an identification comparison using Fisher kernels and a Hidden Markov Model on limited data.