  Modelling human perception of musical similarity is critical for the
evaluation of generative music systems, musicological research, and many Music
Information Retrieval tasks. Although human similarity judgments are the gold
standard, computational analysis is often preferable, since results are often
easier to reproduce, and computational methods are much more scalable.
Moreover, computation based approaches can be calculated quickly and on demand,
which is a prerequisite for use with an online system. We propose StyleRank, a
method to measure the similarity between a MIDI file and an arbitrary musical
style delineated by a collection of MIDI files. MIDI files are encoded using a
novel set of features and an embedding is learned using Random Forests.
Experimental evidence demonstrates that StyleRank is highly correlated with
human perception of stylistic similarity, and that it is precise enough to rank
generated samples based on their similarity to the style of a corpus. In
addition, similarity can be measured with respect to a single feature, allowing
specific discrepancies between generated samples and a particular musical style
to be identified.
