  A range of applications of multi-modal music information retrieval is centred
around the problem of connecting large collections of sheet music (images) to
corresponding audio recordings, that is, identifying pairs of audio and score
excerpts that refer to the same musical content. One of the typical and most
recent approaches to this task employs cross-modal deep learning architectures
to learn joint embedding spaces that link the two distinct modalities - audio
and sheet music images. While there has been steady improvement on this front
over the past years, a number of open problems still prevent large-scale
employment of this methodology. In this article we attempt to provide an
insightful examination of the current developments on audio-sheet music
retrieval via deep learning methods. We first identify a set of main challenges
on the road towards robust and large-scale cross-modal music retrieval in real
scenarios. We then highlight the steps we have taken so far to address some of
these challenges, documenting step-by-step improvement along several
dimensions. We conclude by analysing the remaining challenges and present ideas
for solving these, in order to pave the way to a unified and robust methodology
for cross-modal music retrieval.
