通过元学习进行小概率声事件检测（CS SD）
- 2020 年 3 月 17 日
- 筆記
本文研究了小波声事件检测技术。少镜头学习能够用非常有限的标记数据检测新事件。与计算机视觉等其他研究领域相比，语音识别的镜头学习研究较少。我们提出了少镜头AED问题，并探索了不同的方法来利用传统的监督方法，以及各种元学习方法，这些方法通常用于解决少镜头分类问题。与有监督的基线相比，元学习模型具有更好的性能，从而显示了它对新音频事件的泛化效果。我们的分析包括初始化和领域差异的影响，进一步验证了元学习方法在小样本AED中的优势。
原文题目：Few-shot acoustic event detection via meta-learning
原文：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.
原文作者：Bowen Shi, Ming Sun, Krishna C. Puvvada, Chieh-Chi Kao, Spyros Matsoukas, Chao Wang
原文地址：http://cn.arxiv.org/abs/2002.09143
