Using Fisher Kernels and Hidden Markov Models for the Identification of Famous Composers from their Sheet Music
David Hardoon, Craig Saunders and John Shawe-Taylor
none, Southampton, UK.
We present a novel kernel which operates directly on the structural data of music notation.
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.