论文标题
在声音事件识别中改善概括的顺序自我教学方法
A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition
论文作者
论文摘要
机器听觉感知中的一个重要问题是识别和检测声音事件。在本文中，我们提出了一种对学习声音的顺序自学方法。我们的主要主张是，在不利的情况下，例如从弱标记和/或嘈杂的标记数据中学习声音更加困难，在这些情况下，单个学习阶段是不够的。我们的建议是一个顺序的阶段学习过程，可提高给定建模系统的概括能力。我们通过技术结果证明了这种方法，并且在Audioset（最大的声音事件数据集）上，我们的顺序学习方法可导致性能提高9％。全面的评估还表明，该方法可提高知识从以前训练的模型中的可传递性，从而提高了转移学习任务的概括能力。
An important problem in machine auditory perception is to recognize and detect sound events. In this paper, we propose a sequential self-teaching approach to learning sounds. Our main proposition is that it is harder to learn sounds in adverse situations such as from weakly labeled and/or noisy labeled data, and in these situations a single stage of learning is not sufficient. Our proposal is a sequential stage-wise learning process that improves generalization capabilities of a given modeling system. We justify this method via technical results and on Audioset, the largest sound events dataset, our sequential learning approach can lead to up to 9% improvement in performance. A comprehensive evaluation also shows that the method leads to improved transferability of knowledge from previously trained models, thereby leading to improved generalization capabilities on transfer learning tasks.
