  We study few-shot acoustic event detection (AED) in this paper. Few-shot
learning enables detection of new events with very limited labeled data.
Compared to other research areas like computer vision, few-shot learning for
audio recognition has been under-studied. We formulate few-shot AED problem and
explore different ways of utilizing traditional supervised methods for this
setting as well as a variety of meta-learning approaches, which are
conventionally used to solve few-shot classification problem. Compared to
supervised baselines, meta-learning models achieve superior performance, thus
showing its effectiveness on generalization to new audio events. Our analysis
including impact of initialization and domain discrepancy further validate the
advantage of meta-learning approaches in few-shot AED.
