  Music transcription, which deals with the conversion of music sources into a
structured digital format, is a key problem for Music Information Retrieval
(MIR). When addressing this challenge in computational terms, the MIR community
follows two lines of research: music documents, which is the case of Optical
Music Recognition (OMR), or audio recordings, which is the case of Automatic
Music Transcription (AMT). The different nature of the aforementioned input
data has conditioned these fields to develop modality-specific frameworks.
However, their recent definition in terms of sequence labeling tasks leads to a
common output representation, which enables research on a combined paradigm. In
this respect, multimodal image and audio music transcription comprises the
challenge of effectively combining the information conveyed by image and audio
modalities. In this work, we explore this question at a late-fusion level: we
study four combination approaches in order to merge, for the first time, the
hypotheses regarding end-to-end OMR and AMT systems in a lattice-based search
space. The results obtained for a series of performance scenarios -- in which
the corresponding single-modality models yield different error rates -- showed
interesting benefits of these approaches. In addition, two of the four
strategies considered significantly improve the corresponding unimodal standard
recognition frameworks.
