  Several recent polyphonic music transcription systems have utilized deep
neural networks to achieve state of the art results on various benchmark
datasets, pushing the envelope on framewise and note-level performance
measures. Unfortunately we can observe a sort of glass ceiling effect. To
investigate this effect, we provide a detailed analysis of the particular kinds
of errors that state of the art deep neural transcription systems make, when
trained and tested on a piano transcription task. We are ultimately forced to
draw a rather disheartening conclusion: the networks seem to learn combinations
of notes, and have a hard time generalizing to unseen combinations of notes.
Furthermore, we speculate on various means to alleviate this situation.
