论文标题
学习通用特征表示，并具有合成数据，可通过框架间距离损失进行弱监督的声音事件检测
Learning generic feature representation with synthetic data for weakly-supervised sound event detection by inter-frame distance loss
论文作者
论文摘要
由于强烈标记的声音事件检测数据集的局限性，使用合成数据改善声音事件检测系统性能已成为新的研究重点。在本文中，我们尝试利用合成数据的使用来改善特征表示。基于公制学习，我们提出了针对域适应的框架间距离损耗函数，并证明了IT对声音事件检测的有效性。我们还使用合成数据应用了多任务学习。我们发现，当两种方法一起使用时，可以实现最佳性能。 Dcase 2018 Task 4测试集和Dcase 2019 Task 4合成集的实验均显示竞争成果。
Due to the limitation of strong-labeled sound event detection data set, using synthetic data to improve the sound event detection system performance has been a new research focus. In this paper, we try to exploit the usage of synthetic data to improve the feature representation. Based on metric learning, we proposed inter-frame distance loss function for domain adaptation, and prove the effectiveness of it on sound event detection. We also applied multi-task learning with synthetic data. We find the the best performance can be achieved when the two methods being used together. The experiment on DCASE 2018 task 4 test set and DCASE 2019 task 4 synthetic set both show competitive results.
