  This work addresses the problem of matching short excerpts of audio with
their respective counterparts in sheet music images. We show how to employ
neural network-based cross-modality embedding spaces for solving the following
two sheet music-related tasks: retrieving the correct piece of sheet music from
a database when given a music audio as a search query; and aligning an audio
recording of a piece with the corresponding images of sheet music. We
demonstrate the feasibility of this in experiments on classical piano music by
five different composers (Bach, Haydn, Mozart, Beethoven and Chopin), and
additionally provide a discussion on why we expect multi-modal neural networks
to be a fruitful paradigm for dealing with sheet music and audio at the same
time.
