论文标题
通过元学习的几次声学事件检测
Few-shot acoustic event detection via meta-learning
论文作者
论文摘要
在本文中，我们研究了很少的声学事件检测（AED）。很少有学习的学习能够检测具有非常有限的标记数据的新事件。与其他研究领域（例如计算机视觉）相比，几乎没有用于识别音频识别的学习。我们制定了很少的AED问题，并探索了在此设置中利用传统监督方法以及各种元学习方法的不同方法，这些方法通常用于解决一些少量分类问题。与受监督的基线相比，元学习模型具有出色的性能，从而显示出对新音频事件的概括的有效性。我们的分析包括初始化和域差异的影响，进一步验证了元学习方法的优势。
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.
