This paper presents a new learning strategy for the Sound Event Detection (SED) system to tackle the issues of i) knowledge migration from a pre-trained model to a new target model and ii) learning new sound events without forgetting the previously learned ones without re-training from scratch. In order to migrate the previously learned knowledge from the source model to the target one, a neural adapter is employed on the top of the source model. The source model and the target model are merged via this neural adapter layer. The neural adapter layer facilitates the target model to learn new sound events with minimal training data and maintaining the performance of the previously learned sound events similar to the source model. Our extensive analysis on the DCASE16 and US-SED dataset reveals the effectiveness of the proposed method in transferring knowledge between source and target models without introducing any performance degradation on the previously learned sound events while obtaining a competitive detection performance on the newly learned sound events.

中文翻译：

---

声音事件检测的增量学习算法

本文为声音事件检测 (SED) 系统提出了一种新的学习策略，以解决 i) 从预训练模型到新目标模型的知识迁移以及 ii) 在不忘记先前学习的情况下学习新的声音事件的问题从头开始重新训练。为了将先前学习的知识从源模型迁移到目标模型，在源模型的顶部采用了神经适配器。源模型和目标模型通过这个神经适配器层合并。神经适配器层有助于目标模型以最少的训练数据学习新的声音事件，并保持与源模型类似的先前学习的声音事件的性能。