  Data-driven approaches to automatic drum transcription (ADT) are often
limited to a predefined, small vocabulary of percussion instrument classes.
Such models cannot recognize out-of-vocabulary classes nor are they able to
adapt to finer-grained vocabularies. In this work, we address open vocabulary
ADT by introducing few-shot learning to the task. We train a Prototypical
Network on a synthetic dataset and evaluate the model on multiple real-world
ADT datasets with polyphonic accompaniment. We show that, given just a handful
of selected examples at inference time, we can match and in some cases
outperform a state-of-the-art supervised ADT approach under a fixed vocabulary
setting. At the same time, we show that our model can successfully generalize
to finer-grained or extended vocabularies unseen during training, a scenario
where supervised approaches cannot operate at all. We provide a detailed
analysis of our experimental results, including a breakdown of performance by
sound class and by polyphony.
