Scaling Polyphonic Transcription with Mixtures of Monophonic Transcriptions

Ian Simon
Josh Gardner
Curtis Glenn-Macway Hawthorne
ISMIR 2022


Automatic Music Transcription (AMT), in particular the problem of automatically extracting notes from audio, has seen much recent progress via the training of neural network models on musical audio recordings paired with aligned ground-truth note labels. However, progress is currently limited by the difficulty of obtaining such note labels for natural audio recordings at scale. In this paper, we take advantage of the fact that for monophonic music, the transcription problem is much easier and largely solved via modern pitch-tracking methods. Specifically, we show that we are able to combine recordings of real monophonic music (and their transcriptions) into artificial and musically-incoherent mixtures, greatly increasing the scale of labeled training data. By pretraining on these mixtures, we can use a larger neural network model and significantly improve upon the state of the art in multi-instrument polyphonic transcription. We demonstrate this improvement across a variety of datasets and in a ``zero-shot'' setting where the model has not been trained on any data from the evaluation domain.

Research Areas