  We explore a novel way of conceptualising the task of polyphonic music
transcription, using so-called invertible neural networks. Invertible models
unify both discriminative and generative aspects in one function, sharing one
set of parameters. Introducing invertibility enables the practitioner to
directly inspect what the discriminative model has learned, and exactly
determine which inputs lead to which outputs. For the task of transcribing
polyphonic audio into symbolic form, these models may be especially useful as
they allow us to observe, for instance, to what extent the concept of single
notes could be learned from a corpus of polyphonic music alone (which has been
identified as a serious problem in recent research). This is an entirely new
approach to audio transcription, which first of all necessitates some
groundwork. In this paper, we begin by looking at the simplest possible
invertible transcription model, and then thoroughly investigate its properties.
Finally, we will take first steps towards a more sophisticated and capable
version. We use the task of piano transcription, and specifically the MAPS
dataset, as a basis for these investigations.
