Sound event detection is to infer the event by understanding the surrounding
environmental sounds. Due to the scarcity of rare sound events, it becomes
challenging for the well-trained detectors which have learned too much prior
knowledge. Meanwhile, few-shot learning methods promise a good generalization
ability when facing a new limited-data task. Recent approaches have achieved
promising results in this field. However, these approaches treat each support
example independently, ignoring the information of other examples from the
whole task. Because of this, most of previous methods are constrained to
generate a same feature embedding for all test-time tasks, which is not
adaptive to each inputted data. In this work, we propose a novel task-adaptive
module which is easy to plant into any metric-based few-shot learning
frameworks. The module could identify the task-relevant feature dimension.
Incorporating our module improves the performance considerably on two datasets
over baseline methods, especially for the transductive propagation network.
Such as +6.8% for 5-way 1-shot accuracy on ESC-50, and +5.9% on noiseESC-50. We
investigate our approach in the domain-mismatch setting and also achieve better
results than previous methods.

本研究提出了一种新颖的任务自适应模块，用于度量学习框架的少样本学习中，并在两个数据集上实现了比基线方法取得了更好的性能，特别是在转导传播网络上，例如在 ESC-50 的 5 路 1-shot 准确性上提高了 6.8％，在 noiseESC-50 上提高了 5.9％。