  Automatic music transcription is considered to be one of the hardest problems
in music information retrieval, yet recent deep learning approaches have
achieved substantial improvements on transcription performance. These
approaches commonly employ supervised learning models that predict various
time-frequency representations, by minimizing element-wise losses such as the
cross entropy function. However, applying the loss in this manner assumes
conditional independence of each label given the input, and thus cannot
accurately express inter-label dependencies. To address this issue, we
introduce an adversarial training scheme that operates directly on the
time-frequency representations and makes the output distribution closer to the
ground-truth. Through adversarial learning, we achieve a consistent improvement
in both frame-level and note-level metrics over Onsets and Frames, a
state-of-the-art music transcription model. Our results show that adversarial
learning can significantly reduce the error rate while increasing the
confidence of the model estimations. Our approach is generic and applicable to
any transcription model based on multi-label predictions, which are very common
in music signal analysis.
