  Previous work has shown that neural architectures are able to perform optical
music recognition (OMR) on monophonic and homophonic music with high accuracy.
However, piano and orchestral scores frequently exhibit polyphonic passages,
which add a second dimension to the task. Monophonic and homophonic music can
be described as homorhythmic, or having a single musical rhythm. Polyphonic
music, on the other hand, can be seen as having multiple rhythmic sequences, or
voices, concurrently. We first introduce a workflow for creating large-scale
polyphonic datasets suitable for end-to-end recognition from sheet music
publicly available on the MuseScore forum. We then propose two novel
formulations for end-to-end polyphonic OMR -- one treating the problem as a
type of multi-task binary classification, and the other treating it as
multi-sequence detection. Building upon the encoder-decoder architecture and an
image encoder proposed in past work on end-to-end OMR, we propose two novel
decoder models -- FlagDecoder and RNNDecoder -- that correspond to the two
formulations. Finally, we compare the empirical performance of these end-to-end
approaches to polyphonic OMR and observe a new state-of-the-art performance
with our multi-sequence detection decoder, RNNDecoder.
