  Automatic Music Transcription (AMT), inferring musical notes from raw audio,
is a challenging task at the core of music understanding. Unlike Automatic
Speech Recognition (ASR), which typically focuses on the words of a single
speaker, AMT often requires transcribing multiple instruments simultaneously,
all while preserving fine-scale pitch and timing information. Further, many AMT
datasets are "low-resource", as even expert musicians find music transcription
difficult and time-consuming. Thus, prior work has focused on task-specific
architectures, tailored to the individual instruments of each task. In this
work, motivated by the promising results of sequence-to-sequence transfer
learning for low-resource Natural Language Processing (NLP), we demonstrate
that a general-purpose Transformer model can perform multi-task AMT, jointly
transcribing arbitrary combinations of musical instruments across several
transcription datasets. We show this unified training framework achieves
high-quality transcription results across a range of datasets, dramatically
improving performance for low-resource instruments (such as guitar), while
preserving strong performance for abundant instruments (such as piano).
Finally, by expanding the scope of AMT, we expose the need for more consistent
evaluation metrics and better dataset alignment, and provide a strong baseline
for this new direction of multi-task AMT.
